Factors associated with non-frequent breakfast consumption in adolescents (EVA-JF Study)

Fatores associados ao consumo não frequente de café da manhã em adolescentes (Estudo EVA-JF)

ABSTRACT

Objective

To estimate the association of infrequent breakfast consumption with socioeconomic, behavioral, and individual factors in a sample of Brazilian adolescents from public schools.

Methods

Cross-sectional study with adolescents aged from 14 to 19 from public schools in Juiz de Fora, state of Minas Gerais. The frequency of consumption of breakfast, snacks, soft drinks, industrialized drinks, the usual food consumption, body mass index, body fat percentage, and waist circumference were evaluated. Other socioeconomic, behavioral, and individual data were obtained through questionnaires. Logistic regression analysis and hierarchical selection of variables were used to verify the associated factors.

Results

The sample consisted of 805 adolescents; 53.4% reported infrequent breakfast consumption. Through hierarchical logistic regression analysis, it was evidenced that the house occupancy status (OR: 0.618; 95%CI: 0.4410.865; p=0.005) was the distal factor associated with infrequent breakfast consumption; the intermediate factors were the consumption of industrialized beverages (OR: 0.658; 95%CI: 0.486-0.890; p=0.007) and percentage of energy from processed foods (OR: 0.935; 95%CI: 0.907-0.964; p<0.001); and the proximal factors were the male gender (OR: 0.696; 95%CI: 0.520-0.932; p=0.0015) and being nonwhite (OR: 1.529; 95%CI: 1.131-2.069; p=0.006).

Conclusion

Male adolescents who lived in owned houses, with occasional consumption of industrialized beverages and a higher percentage of energy derived from processed foods, had lower chances of infrequent breakfast consumption, while non-white adolescents had higher chances.

Keywords
Adolescents; Breakfast; Feeding behavior; Socioeconomic factors

RESUMO

Objetivo

Estimar as associações do consumo não frequente de café da manhã com fatores socioeconômicos, comportamentais e individuais em uma amostra de adolescentes de escolas públicas.

Métodos

Estudo transversal realizado com adolescentes de 14 a 19 anos matriculados em escolas públicas de Juiz de Fora (MG). Foram avaliadas a frequência de consumo de café da manhã, lanches, refrigerantes e bebidas industrializadas, consumo alimentar usual, IMC, percentual de gordura corporal e perímetro da cintura. Demais dados socioeconômicos, comportamentais e individuais foram obtidos através de questionários. A análise de regressão logística e seleção hierárquica das variáveis foram usadas para verificar fatores associados.

Resultados

A amostra foi composta por 805 adolescentes e 53,4% deles relataram consumo não frequente de café da manhã. Através da análise de regressão logística hierarquizada, evidenciou-se que a ocupação em domicílio próprio (OR: 0,618; IC95%: 0,441-0,865; p=0,005) foi o fator distal associado ao consumo não frequente de café da manhã, além dos fatores intermediários “consumo não frequente de bebidas industrializadas” (OR: 0,658; IC95%: 0,486-0,890; p=0,007) e “percentual de energia proveniente de alimentos processados” (OR: 0,935; IC95%: 0,907-0,964; p<0,001) e dos fatores proximais “sexo masculino” (OR: 0,696; IC95%: 0,520-0,932; p=0,0015) e “cor da pele não branca” (OR: 1,529; IC95%: 1,131-2,069; p=0,006).

Conclusão

Adolescentes que residiam em domicílios próprios, com consumo não frequente de bebidas industrializadas, com maior percentual de energia proveniente de alimentos processados e do sexo masculino apresentaram menores chances de consumo não frequente de café da manhã, enquanto, adolescentes com cor da pele não branca apresentaram maiores chances.

Palavras-chave
Adolescentes; Desjejum; Comportamento alimentar; Fatores socioeconômicos

INTRODUCTION

From 10 to 19 years of age, adolescence is the transition period between childhood and adult life, when several physical, hormonal, psychological, and behavioral alterations occur [¹,²]. Social, economic, cultural, environmental, behavioral, and psychological factors during this stage may impact the person’s choices and habits, including the eating habits, which will be a part of his or her identity and reflect on morbidity patterns and future health spending [³].

Studies report faulty eating habits among adolescents, such as the elevated consumption of ultra-processed foods like soft and industrialized drinks and fast food, and the omission of fundamental meals, such as breakfast [⁴-⁷]. According to the Dietary Guidelines for the Brazilian Population, breakfast is one of the three most important meals of the day [⁸]. Its consumption is associated with improved anthropometric profiles and body composition, higher diet quality, superior academic performance, and more cognitive capacity [⁹,¹⁰]. On the other hand, its omission or occasional consumption is associated with unfavorable socioeconomic conditions and the development of cardiometabolic risk factors, favoring the development of non-communicable chronic diseases [¹¹,⁷].

Henceforth, understanding the determinant and conditioning potential factors associated with the infrequent consumption of breakfast is essential to base decision-making and plan actions for effectively promoting healthy eating habits, including having breakfast every day. Thus, this study’s objective is to estimate the associations between occasional breakfast consumption and socioeconomic, behavioral, and individual factors in a sample of Brazilian adolescents using hierarchical analysis.

METHODS

This cross-sectional study used data from research conducted with adolescents in the selected municipality (Juiz de Fora, in the Brazilian state of Minas Gerais) named Study of the Lifestyle in Adolescence – Juiz de Fora (EVA-JF Study, Portuguese acronym). We considered adolescents of both sexes, between 14 and 19 years old, who went to public schools in the city’s urban area. Of the 49 schools with students in this age range, 20 were not eligible. To simplify the logistics and reduce the costs related to collecting and processing blood samples, we decided to consider only students enrolled in morning classes.

The sample calculation (n=790) was estimated with the software Epi Info (version 7.2.2.6, Center for Disease Control and Prevention, USA) using the following parameters: 9502 students enrolled in Basic Education in 2018 and 2019 (9th grade in elementary school and 1^st, 2^nd, and 3^rd grades in high school); an 8% prevalence of obesity in the adolescent population; a 2% precision of the prevalence, with a standard error of 1%; confidence interval of 95% (95%CI), and expected losses of 20% [¹²,¹⁴].

The sample was stratified by the city’s administrative regions (Central, Eastern, Northeastern, Northern, Western, Southeastern, and Southern), school year, school facility, class, and sex. The layers’ sample size corresponded to their proportion in the general population (proportional allocation).

To select the participants, the records of the eligible classes were reordered with software that generated random numbers (Stats, version 2.0, Decision Analyst, USA). Adolescents were picked until the necessary number was reached and, in case of refusals or school transference, the next adolescent selected was called.

The data collection was performed by trained professionals in the institutions during the mornings between May 2018 and May 2019. More information on the study is available at Neves et al. [¹⁵].

The work was conducted in compliance with the Declaration of Helsinki and approved by the Ethics Committee in Research of the Federal University of Juiz de Fora (Universidade Federal de Juiz de Fora) (CAEE: 68601617.1.0000.5147). Participation was voluntary. Those above 18 years old signed a Free and Informed Consent Form. Minors signed a Free and Informed Assent Form, and their parents or the responsible adults also signed.

In relation to socioeconomic variables, the analysis considered the age, sex, self-referred race/ethnicity [white and non-white (brown, Black, Indigenous, or Asian-descendant)], house occupancy status (rented/ceded or owned), mother’s schooling (illiterate, incomplete elementary school, incomplete high school, complete high school or college), mother’s current employment status (working or not working), socioeconomic status [medium/high (classes A or B1), medium (B2 or C1), and medium/low (classes C2 or DE), as per the Brazilian Economic Classification Criteria of the Associação Brasileira de Empresas de Pesquisa (Brazilian Association of Research Companies) [¹⁶]. The information was collected with an structured questionnaire applied in person with the adolescents.

The frequency of breakfast consumption was evaluated with the question: “Do you usually have breakfast?” The possible answers included: never; hardly ever; 1 or 2 days a week; 3 or 4 days a week; 5 or 6 days a week; every day. To analyze the frequency, the categories were: not frequent (0 to 4 days a week) and frequent (5 to 7 days a week).

Along with the categorial analysis, a trained team conducted two 24-hour recalls of eating habits on non-consecutive weekdays using the multiple-pass method [¹⁷]. We used a photographic album to estimate the ingested portions and quantities [¹⁸]. The total and macronutrient-related energetic values (both measured in kcal) were assessed with a table of nutritional composition and nutritional labels [¹⁹].

The food items were evaluated according to the NOVA classification system proposed by Monteiro et al. [²⁰], which considers the food items’ degree of industrial processing, dividing them into natural or minimally processed foods; culinary ingredients; processed and ultra-processed foods. In this study, culinary ingredients were grouped with natural or minimally processed foods, as they are usually used in culinary preparations, not in isolation.

Afterwards, to estimate the usual ingestion of food, nutrients, and energy, the data were adjusted in the program Multiple Source Method (version 1.0.1, German Institute of Human Nutrition, Potsdam-Rehbrucke, Department of Epidemiology), reducing intra-individual variation [²¹,²²]. Later, we calculated the average daily energy contribution of each food group according to the NOVA classification.

A questionnaire also verified how often adolescents consumed ultra-processed food items in restaurants or fast-food chains, and soft and other industrialized drinks (powdered juice, juice and teas sold in cans or cartons, flavored water, guarana and currant syrup, energy drinks, fermented milk, chocolate drinks, sweetened and flavored yogurt). The periodicity with which these items were consumed was classified as non-frequent (0 to 4 days a week) and frequent (5 to 7 days a week) consumption, similarly to classification found in other works [²³,²⁴].

The evaluation of the participants’ nutritional status started with measurements of their weight and height for the subsequent calculation of the body mass index. The weight was measured with the Tanita Ironman scale (model BC-553, Tanita Corp., Japan), which has a maximum capacity of 200 kg and a 50 g precision; the height was measured with a portable stadiometer (Alturexata, Brazil), with centimeter scales and 1mm precision, following a standard protocol [¹⁵]. The body mass index was classified within the growth curves of the World Health Organization according to sex and age, expressed in z-score and then categorized as per the weight status variable into non-overweight (z-score <1) and overweight (z-score ≥+1) [²⁵].

The body fat percentage was assessed by bipolar electrical bioimpedance with Tanita Ironman (model BC-553, Tanita Corp., Japan) and classified with Lohman’s cut-off points [¹⁵,²⁶,²⁷]. After, adolescents were classified as at risk or not at risk (≥25% for girls and ≥20% for boys). The waistline was measured once in the intermediary point between the inferior border of the last rib and the iliac crest’s superior limit, or in the smallest diameter between the thorax and the hips (for adolescents who were overweight), with a Sanny measuring tape (American Medical Ltda., Brazil) [²⁸]. As there is no consensus on the specific cut-off points for adolescents waist circumference, the risk classification was attributed to those with measures ≥the 90^th percentile of the sample, according to sex and age. All anthropometric and body composition assessments were conducted by a properly trained health professional [²⁹,³⁰].

To estimate the regular practice of physical education in the 12 months before the research, we used the International Physical Activity Questionnaire, a validated instrument that measures the frequency and type of exercise, as well as the time spent exercising in a habitual week [³¹,³²]. Adolescents who exercised for more than 300 minutes a week (considering the five usual weekdays, excluding weekends) were understood as physically active [³³].

Information regarding the total hours of sleep per night during weekdays was collected with the Pittsburgh Sleep Quality Index, which assessed sleep quality and duration [³⁴]. According to the National Sleep Foundation, adolescents must sleep 8 to 10 hours a night [³⁵]. Thus, sleep was classified as inadequate when <8 hours/night (<480 minutes) or adequate when ≥8 hours/night (>480 minutes).

The data were analyzed with the software SPSS^®IBM^® (version 20.0), with a significance level of 5% (p<0.05).

First, the quantitative continuous variables underwent the normality Kolmogorov-Smirnov test, with the normality parameters expressed with measures of central tendency (means) and dispersion (standard deviation). The qualitative variables were described with absolute (n) and relative (%) frequencies. To examine the factors associated with the non-frequent consumption of breakfast, we used hierarchical multiple logistic regression. For the hierarchization of variables, we established and maintained a conceptual model during the data analysis [³⁶,³⁷].

The literature does not offer a specific model to determine the frequency of breakfast consumption. The model we created for this purpose was based on the conceptual model proposed by Dahlgren and Whitehead [³⁸], which approaches social determinants of health, and the one proposed by Alexandre et al. [³⁹], discussing the variables influencing the adoption of healthy eating choices.

The hierarchical analysis was conducted as soon as the conceptual model of independent variables was established (Figure 1). First, we performed a univariate logistic regression considering a 95%CI. Then, the multivariate analysis started from the hierarchical entry into groups of variables that presented a statistical significance below 20% (p<0.20) in the previous stage, ordered as follows: Group 1: Socioeconomic characteristics; Group 2: Behavioral characteristics; Group 3: individual characteristics. The frequency of breakfast consumption (frequent or not frequent) was a dependent variable in every stage.

Figure 1
Conceptual hierarchical model for determining the factors associated with infrequent breakfast consumption.

The backward LR method, employed in the hierarchical multiple logistic regression analysis, initially incorporates all the variables in each group separately, which may be eliminated in later stages depending on the results of F partial tests until the final model is produced. To interpret the results, we considered p<0.05. The statistical significances were obtained with the Wald test for heterogeneity. The Hosmer-Lemeshow test evaluated the final model’s consistency, considering the adjustment adequate when p>0.05. To assess the model’s explanatory power, we used the Nagelkerke R Square test.

RESULTS

The initial number of adolescents participating the EVA-JF study was 835. However, with the losses referring to the lack of data regarding food consumption, the study’s final sample consisted of 805 adolescents. Table 1 presents the description of the participants’ socioeconomic, behavioral, and individual characteristics, including the frequency of breakfast consumption. Most participants (53.4%) reported not having breakfast frequently (0 to 4 days a week).

Table 1
Adolescents’ socioeconomic, behavioral, and individual characteristics. Juiz de Fora (MG), Brazil, 2018-2019.

Group 3		Socioeconomic characteristics
		n	%
Socioeconomic status ♠
	Medium/low	161	20.0
	Medium	471	58.5
	Medium/high	173	21.5
House occupancy status
	Rent/concession	202	25.1
	Ownership	603	74.9
Mother’s employment status
	Not working	227	29.0
	Working	556	71.0
Mother’s education
	Illiterate or incomplete elementary school	30	4.2
	Complete elementary school/ incomplete high school	31	43.0
	High school	273	37.9
	University	108	15.0
Group 2		Behavioral characteristics
		n	% or M(SD)a †
Level of physical activity ●
	Insufficiently active	287	35.7
	Active	518	64.3
Hours of sleep per night ┤
	Inadequate	133	16.5
	Adequate	672	83.5
Consumption of ultra-processed foods in restaurants or fast-food chains δ
	Frequent	35	4.3
	Not frequent	770	95.7
	Consumption of soda δ
	Frequent	139	17.3
	Not frequent	666	82.7
Consumption of industrialized drinks δ
	Frequent	289	35.9
	Not frequent	516	64.1
% of energy from ultra-processed foods		---	45.7 (12.9) a
% of energy from processed foods		---	10.9 (4.9) a
% of energy from natural or minimally processed foods		---	43.3 (12.1) a
Group 1		Individual characteristics
		n	%
Sex
	Female	464	57.6
	Male	341	42.4
Age range ‡
	14 and 15	248	30.8
	16 and 17	456	56.6
	18 and 19	101	12.5
Race/ethnicity
	White	281	35.2
	Non-white ⸸	517	64.8
Weight status
	Not overweight	563	71.3
	Overweight	227	28.7
% of body fat ||
	Not at risk	346	51.0
	At risk	332	49.0
Waist circumference ¶
	Not at risk	723	89.8
	At risk	82	10.2
Frequency of consumption δ		Breakfast
		n	%
	Frequent	375	46.6
	Not frequent	430	53.4

• Note:
• ^a
  M (SD).
• ^†
  Valid percentages, considering eventual losses.
• ^♠
  Medium/low: classes C2 or D-E. Medium: classes B2 or C1. Medium/high: classes A or B1.
• ^●
  Insufficiently active: <300 minutes/week. Active: ≥300 minutes/week.
• ^┤
  Inadequate: <480 minutes. Adequate: ≥480 minutes.
• ^δ
  Frequent: 5 to 7 days/week. Not frequent: 0 to 4 days/week.
• ^‡
  Average age of 16.1 years (SD=1.2).
• ^⸸
  Non-white: brown, Black, Indigenous, or Asian.
• ^||
  Not at risk <25% (female) and <20% (male). At risk: ≥25% (female) and ≥20% (male).
• ^¶
  Not at risk: <90^th percentile of the sample, according to sex. At risk: ≥90^th percentile of the sample, according to sex. M: Mean; SD: Standard-Deviation.

Table 2 presents the socioeconomic, behavioral, and individual characteristics associated with non-frequent consumption of breakfast, according to the univariate logistic regression analysis. All the variables that presented p>0.20 were selected for the analysis of hierarchical regression.

Table 2
Model of univariate logistic regression explaining non-frequent consumption of breakfast among adolescents. Juiz de Fora (MG), Brazil, 2018-2019.

Group 3		Socioeconomic characteristics
		OR	95% CI	p *
Socioeconomic status ♠
	Middle/low	Reference
	Middle	0.660	0.425-1.011	0.050
	Middle/high	0.790	0.552-1.139	0.209
House occupancy status
	Rent/concession	Reference
	Ownership	0.633	0.460-0.871	0.005
Mother’s employment status
	Not working	Reference
	Working	0.931	0.687-1.262	0.645
Mother’s education
	Illiterate or incomplete elementary school	Reference
	Complete elementary school/ incomplete high school	1.190	0.587-2.414	0.629
	High school	1.058	0.519-2.156	0.876
	University	0.897	0.415-1.935	0.781
Group 2		Behavioral characteristics
		OR	95% CI	p *
Level of physical activity ●
	Insufficiently active	Reference
	Active	1.075	0.805-1.435	0.626
Hours of sleep per night ┤
	Inadequate	Reference
	Adequate	0.940	0.650-1.351	0.720
Consumption of ultra-processed foods in restaurants or fast-food chains δ
	Frequent	Reference
	Not frequent	1.502	0.746-3.024	0.255
Consumption of soda δ
	Frequent	Reference
	Not frequent	0.734	0.506-1.064	0.103
Consumption of industrialized drinks δ
	Frequent	Reference
	Not frequent	0.635	0.471-0.856	0.002
% of energy from ultra-processed foods		1.012	1.002-1.024	0.025
% of energy from processed foods		0.933	0.906-0.961	<0.001
% of energy from natural or minimally processed foods		0.997	0.986-1.008	0.593
Group 1		Individual characteristics
		OR	95% CI	p *
Sex
	Female	Reference
	Male	0.617	0.468-0.813	0.001
Age group
	14 and 15	Reference
	16 and 17	0.894	0.658-1.215	0.474
	18 and 19	1.011	0.643-1.589	0.963
Group 1		Individual characteristics
		OR	95% CI	p *
Race/ethnicity
	White	Reference
	Non white ⸸	1.670	1.253-2.225	<0.001
Weight status
	Overweight	Reference
	Not overweight	0.821	0.602-1.121	0.214
% of body fat ||
	At risk	Reference
	Not at risk	0.734	0.541-0.995	0.046
Waist circumference ¶
	At risk	Reference
	Not at risk	0.709	0.444-1.131	0.149

• Note:
• ^*
  The statistical significances were obtained with the Wald test for heterogeneity.
• ^♠
  Medium/low: classes C2 or D-E. Medium: classes B2 or C1. Medium/high: classes A or B1.
• ^●
  Insufficiently active: <300 minutes/week. Active: ≥300 minutes/week.
• ^┤
  Inadequate: <480 minutes. Adequate: ≥480 minutes.
• ^δ
  Frequent: 5 to 7 days/week. Not frequent: 0 to 4 days/week.
• ^⸸
  Non-white: brown, Black, Indigenous, or Asian.
• ^||
  At risk: <25% (female) and <20% (male). Not at risk: ≥25% (female) and ≥20% (male).
• ^¶
  Not at risk: <90^th percentile, according to sex. At risk: ≥90^th percentile, according to sex. 95% CI: Confidence Interval of 95%; OR: Odds Ratio.

Through the final model of hierarchical multiple logistic regression shown in Table 3, we observed that the single socioeconomic variable in Group 3 that is associated with non-frequent consumption of breakfast was house occupancy status. Adolescents living in owned homes presented smaller chances of not consuming breakfast frequently (OR: 0.618; 95%CI: 0.441-0.865; p=0.005).

Regarding the behavioral variables present in Group 2, the outcome was negatively associated with the consumption of industrialized drinks (OR: 0.658; 95%CI: 0.486-0.890; p=0.007) and the percentage of ingestion of processed foods (OR: 0.935; 95%CI: 0.907-0.964; p <0.001), which reduced the chances of incurrence.

As to the individual variables in Group 3, both sex (OR: 0.696; 95%CI: 0.520–0.932; p=0.015) and race/ethnicity (OR: 1.529; 95%CI: 1.131-2.069; p=0.006) were associated with the outcome, with male adolescents presenting reduced chances of non-frequent breakfast consumption, while nonwhite adolescents had greater chances.

In table 3, the Hosmer and Lemeshow tests are also described, demonstrating the adequate adjustment of the final model (p=0.723). The explanatory power was of about 9%, according to the Nagelkerke R Square test.

Table 3
Final model of hierarchical multiple logistic regression explaining the non-frequent consumption of breakfast among adolescents. Juiz de Fora (MG), Brazil.

Group 3		Socioeconomic characteristics
		OR	95% CI	p *
House occupancy status
	Rent/concession	Reference
	Ownership	0.618	0.441-0.865	0.005
Group 2		Behavioral characteristics
		OR	95% CI	p *
Consumption of industrialized drinks δ
	Frequent	Reference
	Not frequent	0.658	0.486-0.890	0.007
	% of energy from processed foods	0.935	0.907-0.964	<0.001
Group 1		Individual characteristics
		OR	95% CI	p *
Sex
	Female	Reference
	Male	0.696	0.520-0.932	0.015
Race/ethnicity ⸸
	White	Reference
	Non-white	1.529	1.131-2.069	0.006

• Note: Hosmer and Lemeshow Test=0.723; Nagelkerke R Square=0.089.
• ^*
  The statistical significances were obtained with the Wald test for heterogeneity.
• ^δ
  Frequent: 5 to 7 days/week. Not frequent: 0 to 4 days/week.
• ^⸸
  Non-white: brown, Black, Indigenous, or Asian. 95% CI: Confidence Interval of 95%; OR: Odds Ratio.

DISCUSSION

The results of the present study demonstrate a high prevalence of adolescents who do not have breakfast frequently. The creation of a conceptual model of multiple hierarchical logistic regression evidenced that socioeconomic (house occupancy status), behavioral (consumption of industrialized drinks and percentage of energy from processed items), and individual (male sex and being nonwhite) variables were associated to not having breakfast frequently.

The percentage of adolescents who did not have breakfast frequently in this study (53.4%) was similar to that found in the Study of Estudo de Riscos Cardiovasculares em Adolescentes (Cardiovascular Risks in Adolescents), a national health survey in which 51.4% of adolescents reported not having breakfast often or at all [⁴⁰]. Our results were superior to the percentage found by Simões et al. [⁴¹] for the city of Curitiba, in the state of Paraná (41.4%; 95%CI: 36.8-46.1), and by Azeredo et al. [⁴²] (38.1%), referring to 11- to 14-year-old adolescents assessed by the 2012 Pesquisa Nacional de Saúde do Escolar (National Adolescent School-based Health Survey). These differences may be due to methodological variations, such as the definition of frequency and the number of evaluated days.

The hierarchical regression analysis demonstrated that the only socioeconomic variable, that is, the single distal factor, associated with non-frequent breakfast consumption was house occupancy status. Adolescents who resided in owned houses had smaller chances of not having breakfast frequently. Along with other factors, such as the house’s structural characteristics and the family’s buying power, this variable is employed as an indirect indicator of socioeconomic status [⁴³]. In the present study, the socioeconomic status and the mother’s schooling were not associated with the outcome. However, some authors demonstrated that not having breakfast frequently was significantly more common among children and adolescents with lower socioeconomic status or whose mothers had fewer years of schooling [¹¹,⁴⁴-⁴⁷].

Some studies also pointed to other socioeconomic variables associated with poorer dietary behavior. Another research with children and adolescents in Juiz de Fora [⁴⁸] concluded that adolescents whose mothers had more children had fewer meals a day. Examining the eating habits of adolescents who participated in the 2009 Pesquisa Nacional de Saúde do Escolar, Tavares et al. [⁴⁹] observed a direct association between the Human Development Index and healthier eating practices, suggesting a possible synergic influence between income, education, and health conditions.

Regarding behavioral variables, in an intermediary position concerning the outcome, the unusual consumption of industrialized drinks and the percentage of energy from processed foods were associated with smaller chances of non-frequent consumption of breakfast in the hierarchical regression final model. Similarly, Ramsay et al. [⁵⁰] noticed that adolescents who did not have breakfast often presented an enlarged contribution of caloric ingestion from items like non-alcoholic industrialized drinks and fruit juices. In a study with Norwegian adolescents, Medin et al. [⁵¹] observed that the irregular consumption of breakfast was associated with enlarged consumption of food items rich in sugar, fat, and sodium, including processed foods like fruit and bar candies, crystallized fruit, and industrialized and ultra-processed foods. Having breakfast is a possible marker of healthy eating [³⁸]. Thus, its omission or infrequent consumption is often related to the augmented consumption of industrialized foods and lower diet quality [⁴⁰,⁴⁵,⁵⁰,⁵¹].

As for Group 3, comprising the individual variables proximal to the outcome, the final conceptual model included sex and race/ethnicity, both with significant association with the outcome. Male adolescents had smaller chances of not having breakfast frequently than female ones. Similarly, other studies [⁵²-⁵⁴] demonstrated that the omission or unusual consumption of breakfast is more common among female children or adolescents. According to these authors, this is possibly so because girls are often more concerned or conscious of their appearance, with a relevant influence of the media on these issues. Reinforcing this hypothesis, in their study with Jordanian adolescents of both sexes, Ali et al. [⁵²] observed that over a third of the participants believed that omitting breakfast would lead to weight loss.

Non-white adolescents had more chances of unusual breakfast consumption. These results are close to those of the research by Affenito et al. [⁵⁵], who worked with data from the National Growth and Health Study, a cohort study with Black and white female children and adolescents. According to them, white girls had breakfast more often than non-white girls. On the other hand, in a cohort study with 809 adolescents who participated in the Estudo Longitudinal de Avaliação Nutricional do Adolescente (Adolescent Nutritional Assessment Longitudinal Study) in the state of Rio de Janeiro, Hassan et al. [⁵⁶] did not find a significant association between race/ethnicity and irregular breakfast consumption. However, it is known that race-related inequalities produce vulnerabilities, especially health-related ones, and more exposure to general risk factors and behaviors [⁵⁷].

Thus, the non-frequent consumption of breakfast is associated with female, non-white adolescents who live in rented or ceded houses, usually have industrialized drinks, and with a lesser proportion of energy derived from processed food items. That shows critical social, behavioral, and individual deficiencies that may be the object of public intervention geared toward adolescents.

Our study’s main positive points were: 1) data collection with rigorous methods and trained professionals; 2) two non-consecutive 24-hour recalls, following the multiple-pass method, and the posterior estimation of eating habits with the Multiple Source Method, which counts on advanced statistical modeling techniques, generating more precise ingestion measures for individuals and populations. The study also presents the following limitations: 1) as a cross-sectional epidemiological study, it cannot establish causal relations, even as it may help produce hypotheses of possible health outcomes; 2) the lack of a conceptual model specifically for the non-frequent consumption of breakfast, which made it necessary to use an adapted model; 3) although the sample is representative, it is composed only of adolescents from public schools in Juiz de Fora, suggesting a cautious approach when extrapolating the results for students in private schools and other Brazilian cities.

CONCLUSION

The results of the present study identified determining sociodemographic, behavioral, and individual factors associated with non-frequent consumption of breakfast. Consequently, directed intervention actions are made possible, such as those targeting awareness regarding the benefits of breakfast, leading to positive health impacts during adolescence and adult life.

• Article based on the master’s thesis of ACO CÂNDIDO, intitled “Consumo de café da manhã e sua associação com determinantes sociais, nutricionais, bioquímicos e pressão arterial entre adolescentes de Juiz de Fora, MG: Estudo EVA-JF”. Universidade Federal de Juiz de Fora; 2021.

How to cite tis article

• Cândido ACO, Neves FS, Faria ER, Netto MP, Oliveira RMS, Cândido APC. Factors associated with non-frequent breakfast consumption in adolescents (EVA-JF Study). Rev Nutr. 2022;35:e210166. https://doi.org/10.1590/1678-9865202235e210166

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Publication Dates

• Publication in this collection
  14 Nov 2022
• Date of issue
  2022

History

• Received
  23 Aug 2021
• Reviewed
  21 June 2022
• Accepted
  29 Aug 2022