Screen-based sedentary behaviors increase unhealthy food intake among children and adolescents

Comportamentos sedentários baseados em tela aumentam o consumo de alimentos não saudáveis entre crianças e adolescentes

Abstract

Introduction:  Cross-sectional studies have established an association between exposure to screen devices and unhealthy food intake among children; however, longitudinal evidence on this topic remains scarce.

Objective:  The aim of the study was to estimate the association between screen use and unhealthy food intake among children and adolescents.

Method:  A longitudinal study whose baseline was carried out in May of 2015, and the follow-ups were performed through July, September, and November (n=451). Participating students from the second to fifth grades of a public school (9.21±1.32 years). Food consumption, physical activity, and screen use were self-reported on the Food Intake and Physical Activity of Schoolchildren questionnaire (Web-CAAFE). Body weight and height were measured for the calculation of body mass index (BMI) z scores, according to sex and age. Statistical analysis included modeling with generalized estimating equations for repeated measures to estimate the incidence rate ratios (IRR) and respective 95% confidence intervals (95%CI).

Results:  Cell phone (IRR=1.14, 95%CI 1.06–1.23), computer (IRR=1.26, 95%CI 1.16–1.37), and video game (IRR=1.29, 95%CI 1.17–1.42) were associated with unhealthy food consumption. Schoolchildren with a frequency of screen use ≥3/day and 1–2/day reported, respectively, a 45% and 13% higher frequency of consumption of unhealthy foods, regardless of sex, age, physical activity, and BMI.

Conclusions:  There was an association between screen use and unhealthy food intake. Cell phones, computers, and video games influenced the consumption of unhealthy foods, while watching TV had no significant effect.

Keywords:
sedentary behavior; eating; child; longitudinal studies

Resumo

Introdução:  A associação entre a exposição a dispositivos de tela e o consumo de alimentos não saudáveis entre crianças e adolescentes tem sido estabelecida em estudos transversais, todavia evidências longitudinais sobre esse assunto permanecem escassas.

Objetivo:  Estimar a associação entre uso de telas e consumo de alimentos não saudáveis entre crianças e adolescentes estudantes de uma escola pública (9,21±1,32 anos).

Método:  Estudo longitudinal com linha de base realizada em maio de 2015 e follow-ups realizados em julho, setembro e novembro (n=451). Consumo alimentar, atividade física e uso de telas foram autorreferidos em um questionário online (Consumo Alimentar e Atividade Física de Escolares — Web-CAAFE). Peso corporal e altura foram mensurados para o cálculo dos escores z do índice de massa corporal (IMC), conforme sexo e idade. A análise estatística incluiu modelagem com equações de estimativa generalizada para medidas repetidas para estimar incidence rate ratios (IRR) e respectivos intervalos de confiança de 95% (IC95%).

Resultados:  Uso de celular (IRR=1,14, IC95% 1,06–1,23), computador (IRR=1,26, IC95% 1,16–1,37), e videogame (IRR=1,29, IC95% 1,17–1,42) associaram-se com o consumo de alimentos não saudáveis. Estudantes com frequências de uso de telas ≥3/dia e 1–2/dia relataram, respectivamente, consumo 45 e 13% maior de alimentos não saudáveis, independentemente do sexo, idade, atividade física e IMC.

Conclusão:  Houve associação entre uso de telas e consumo de alimentos não saudáveis. Usar celular, computador e videogame influenciou o consumo de alimentos não saudáveis, enquanto assistir à TV não teve efeito significativo.

Palavras-chave:
comportamento sedentário; ingestão de alimentos; criança; estudos longitudinais

INTRODUCTION

Changes in industrial food processing and in the speed of preparing snacks, with the aim of making them more attractive, durable, and practical for consumption, have been the target of concern due to the association of these foods with unfavorable health outcomes¹.

Fast snacks with a high saturated fat content (fast food), sugary drinks (purchased bottled with high durability and sugar content), sweets, and ultra-processed foods (UPFs) (with excess salt, saturated fat, and food additives) are not considered healthy due to high caloric density, low vitamin and mineral content, and excess industrial ingredients².

Foods such as stuffed cookies, "packaged snacks," instant noodles, boxed juices, soft drinks, chocolate drinks, and French fries, among others, are available in diversified forms and have been integrated into the food systems of contemporary societies. The consumption of unhealthy foods is associated with negative health outcomes for children and adolescents, including dyslipidemia³, asthma⁴, and excess weight⁵.

Similar to the consumption of unhealthy foods, sedentary behaviors, especially screen use, also expose children and adolescents to lower levels of cardiorespiratory fitness, lower insulin sensitivity⁶, unfavorable body composition, unfavorable prosocial behavior, low selfesteem⁷, anxiety⁸, and depression⁹.

The association between the accumulation of sedentary behavior and the consumption of unhealthy foods contributes to the occurrence of cardiovascular and metabolic diseases at increasingly early ages¹⁰. Studies focusing on the association between exposure to screens and the consumption of unhealthy foods are particularly important because, currently, the time children and teenagers dedicate to the use of smartphones and video games is quite significant. According to data from the Common Sense Census, in 2019, in the United States, children and teenagers between 8 and 12 years old are in front of screens for 4 h and 44 min a day, on average. Among teenagers aged 13–18, this reality is more worrying because the use of screens exceeds 7 h a day¹¹.

In general, cross-sectional studies show that there is an association of exposure time to screens of over 2 h/day (TV, computer, video games) with increased consumption of fast food and sugary drinks and, at the same time, lower consumption of fruits and vegetables¹² ,¹³. Findings from longitudinal studies support these results, demonstrating that there is an increase in the consumption of unhealthy foods and a reduction in fruit and vegetable intake associated with screen time among children and adolescents in the United States², and that long TV exposure time among Danish children and adolescents leads to unhealthy eating habits and preferences¹⁴.

To our knowledge, no longitudinal studies have been performed in Brazil addressing the association between screen use and the consumption of unhealthy foods, especially among children aged 7–10 years. Brazilian school-based cross-sectional studies (PeNSE and Study of Cardiovascular Risks in Adolescents—ERICA)¹³,¹⁵ reveal valuable data on the health of adolescents (12–17 years old); however, they do not include children in younger age groups, who may also be more sensitive to health promotion interventions, covering both restriction of exposure to screens and consumption of unhealthy food. Furthermore, as far as we were able to investigate, no studies included an analysis by screen types other than TV or analyzed the effect of smartphone use, which may underestimate the results found, as smartphone use by children has increased since 2016¹⁶. Based on these aspects, the aim of the current study was to estimate the longitudinal association between the daily frequency of screen use and the consumption of unhealthy foods among children and adolescents aged 7–12 years.

METHODS

A longitudinal study was conducted, including four repeated measurements performed during 2015. The baseline occurred in May, and the other repeated measurements were in July, September, and November, with data collection performed on weekdays (from Tuesday to Friday). The convenience sample included children (7–9 years) and adolescents (10–12 years) from a part-time public school in Feira de Santana, Bahia, in the Northeast region of Brazil. The chosen school met the study’s inclusion criteria (having a computer room, broadband internet access, agreement of the management and teaching staff, and provision of school meals). The protocol followed the ethical standards for research with human beings, as per Resolution No. 466/2012 of the National Health Council. Participants received authorization in writing from their parents and signed the Free and Informed Assent Term.

All students from the second to fifth grades of elementary school at the selected school were invited to participate in the study. From a total of 507 invited students, 478 agreed to participate.

Measure of food consumption, physical activities, and sedentary behaviors

At all measurement moments, dietary intake, physical activity, and sedentary data were obtained using the Web-CAAFE, a short questionnaire designed to investigate the daily consumption frequency of specific foods as markers of (un)healthy diet, and types of physical activities and sedentary behaviors on the previous day ( http://caafe.ufsc.br/portal/10/detalhes). The questionnaire is accessed via the internet and was previously validated in two Brazilian cities¹⁷-¹⁹. Food consumption is reported in six meals ordered chronologically (breakfast, morning snack, lunch, afternoon snack, dinner, and evening snack). For each meal, 30 pictures of foods/beverages are presented on the computer screen.

The available foods/beverages or food groups include food icons such as rice, vegetables, green leaves, vegetable soup, beans, cassava flour, noodles, meat/chicken, eggs, fish/seafood, corn/potatoes, bread/biscuits, fruits, porridges, cheese, milk and coffee, and milk and yogurt, and also figures representing unhealthy foods—instant noodles, French fries, sausages, cookies, chocolate milk, fruit juices, carton juice, soft drinks, sweets (such as sweets, chocolate, candy, ice cream, and frosted cakes), packaged snacks, fast food (pizza/burgers/hot dogs, nuggets), and cakes with icing. All items classified as unhealthy foods, due to high caloric density, poor micronutrients, and abundance of salt and sugar, comprised the study outcome variable. The frequencies of consumption of food/beverage items were estimated as the number of times per day (ranging from 0 to 6).

The physical activity/sedentary behaviors section is structured into three periods of the day (morning, afternoon, and night); for each of them 27 leisure activities, sports, gymnastics, and home chores and five icons of sedentary behaviors are presented (four illustrate screen use: TV, computer, video game, and cell phone; and one illustrates reading/writing/drawing/painting while sitting).

Values in metabolic equivalents (METs) were assigned to each physical activity, as per the Energy Costs for Youth Compendium²⁰, and values for all activities reported the day before were added together at the individual level. The Web-CAAFE was applied in the school setting, once at each time point for every child, but on different days considering the whole sample. This strategy was used to describe the daily variability in dietary intake, physical activity, and sedentary behavior, allowing for analysis at the group level.

Anthropometric measurements

Trained researchers took anthropometric measurements of body weight and height of the school children, following recommended standardization²¹. Weight was measured using a Wiso^® digital scale, model Ultra Slim W801, with graduation every 100 g and a maximum capacity of 200 kg. Height was measured using a portable dismountable stadiometer, with a square platform, Seca ^® brand, with a maximum height of 205 cm and graduation every 1 mm. The students were barefoot, wearing their school uniform, and without headwear during the measurements. The body mass index (BMI) values were converted into z-scores (according to age and sex)²². Weight status was categorized as non-overweight (BMI-for-age <+1.0 standard deviation [SD]), overweight (non-obese) (BMI-for-age >+1 SD ≤+2), and obese (BMI-for-age >+2 SD).

Data processing and analysis

For data analysis, the frequency of consumption of unhealthy foods (outcome) was obtained by the sum at the individual level of all reports of instant noodles, French fries, sausages, cookies, chocolate milk, fruit juices, carton juice, soft drinks, sweets, packaged snacks, fast food, and cakes with icing, representing the daily consumption. Students with intellectual disabilities and those outside the age group of 7–12 years old were also excluded.

Descriptive statistics were used to present the study variables. Variables without a normal probability distribution after histogram verification and the Shapiro-Wilk test are described as median and interquartile range values. Participants with one or more daily self-reports of screen use (TV, computer, video game, and cell phone) were classified into an exposed subgroup, whilst the unexposed ones presented no reports of screen use.

To examine relations of TV, computer, video game, and cell phone use with unhealthy food consumption, we used sex-specific multiple binomial negative regression models. Generalized estimating equations (GEE) were used for estimation by specifying an exchangeable covariance structure to account for repeated measures. Models were adjusted for sex, age, BMI z-scores, and METs of daily physical activity.

All reports of TV, computer, video game, and cell phone use were added up at the individual level and divided into tertiles, with the first tertile, second tertile, and third tertile referring to 0,1 to 2, and ≥3 screen use (daily sum), respectively. Then we examined the aggregated relation of screen use with the consumption of unhealthy foods using GEE modeling (binomial negative probability distribution). Effect modification was tested using interaction terms between exposure variables and sex, age, BMI z scores, and METs of daily physical activity. Interactions that showed statistical significance at the critical value of p<0.05 were described.

RESULTS

In total, 478 students participated in the study, authorized by their parents, and 461 (96.4%) were present at the school on the days of data collection. After excluding data from only one individual with intellectual disability and from 33 subjects outside the age group of 7–12 years old, the analytical sample consisted of 451 students. These exclusions consisted of some of the same individuals at each data collection time.

Table 1 presents the characteristics of the sample at baseline and at follow-up moments. The excluded individuals had characteristics similar to those maintained in the data analysis with respect to distribution by sex, age, BMI z scores, and METs of daily physical activities. The association between the daily frequency of screen use and the consumption of unhealthy foods was evaluated by the values of the exponential regression coefficients (β²), representing the incidence rate ratios (IRR). Cell phone use, computer, and video game were associated with unhealthy food consumption (Table 2).

Table 1
Demographic data for children (7–9 years) and adolescents (10–12 years) from public schools throughout 1-year follow-up (n=452).

Variables		May (baseline)	July	September	November
		(n=373)*	(n=408)†	(n=399)†	(n=385)‡
Sex
	Male	53.6%	54.2%	54.9%	54.0%
	Female	46.4%	45.8%	45.1%	46.0%
Age (years)
	7–9	63.3%	56.9%	56.6%	50.9%
	10–12	36.7%	43.1%	43.4%	49.9%
z-BMIa		0.54±1.34	0.52±1.31	0.55±1.31	0.59±1.30
METb		10.8 (0–200.5)	10.5 (0–180.6)	13.6 (0–147.1)	11.3 (0–126.1)
Unhealthy Food Intakec		3.5 (0–21)	4.0 (0–20)	4.0 (0–20)	4.0 (0–27)
TV		47.2%	53.4%	46.4%	51.8%
Cell phone		37.9%	49.0%	46.6%	48.2%
Computer		30.6%	33.3%	27.6%	28.8%
Video game		17.3%	19.2%	18.0%	18.1%
Screen use
	≥3	28.7%	37.9%	31.8%	31.9%
	1–2	42.5%	39.7%	41.6%	44.8%
	0	28.7%	22.4%	25.6%	23.3%

• ^a
  Mean±standard deviation
• ^b
  Median (minimum–maximum): sum of all physical activities performed on the previous day, as MET values
• ^c
  Median (minimum–maximum): sum of all unhealthy food consumed on the previous day
• ^*
  Eight subjects excluded (one with intellectual disability; seven outside of the 7–12 years age group)
• ^†
  Nine subjects were excluded (one with intellectual disability; eight outside of the 7–12 years age group)
• ^‡
  11 subjects excluded (one with intellectual disability; 10 outside of the 7–12 years age group)

Table 2
Adjusted change (IRR and 95% confidence intervals) between screen use and unhealthy foods intakea .

Screen use (types)		Unhealthy food intake
TV		1.01 (0.94–1.09)
Cell phone		1.14 (1.06–1.23)*
Computer		1.26 (1.16–1.37)**
Videogame		1.29 (1.17–1.42)**
Screen use (daily sum)b
	≥3 (3° Tertil)	1.45 (1.29–1.62)**
	1–2 (2° Tertil)	1.13 (1.02–1.26)*

• ^a
  All values are IRR; 95%CIs in parentheses
• Values were estimated by using sex-stratified binomial negative regression models (with the use of generalized estimating equations). Models were adjusted for sex, age, BMI z score, and METs of daily physical activity
• ^b
  All reports of TV, computer, video game, and cell phone use were added up at the individual level and divided into tertiles, with the first tertile, second tertile, and third tertile, referring to 0, 1 to 2, and ≥3 screens, respectively *p<0.01, **p<0.001

Overall, schoolchildren with self-reports of screen use of ≥3/day and 1–2/day on the previous day also reported, respectively, a 45 and 13% higher frequency of consumption of unhealthy foods compared to peers who reported no screens, regardless of sex, age, physical activity, and BMI. There was no significant association between TV use and unhealthy food intake. Moreover, there was no interaction between computer, cell phone, video game, or tertiles of screen use with sex, age, physical activity, and BMI.

We also analyzed the effect of the data collection period (May, July, September, and November) on unhealthy food intake and potential interactions with TV, cell phone, computer, and video game, as well as with tertiles of screen use (details not presented). However, the results showed no significant interactions; that is, there was no longitudinal variation on unhealthy food intake (Figures 1 and 2).

Figure 1
Longitudinal variation on daily frequency of unhealthy food intake stratified by TV, computer, cell phone, and video game use among schoolchildren.

Figure 2
Longitudinal variation on daily frequency of unhealthy food intake stratified by tertiles of screen use (daily sum) among schoolchildren.

DISCUSSION

To our knowledge, this is the first longitudinal study to analyze the association between screen use and the consumption of unhealthy foods among Brazilian children aged 7–12 years. Our results showed that there was no variation on unhealthy food intake during the study period. Moreover, computer use was associated with a higher frequency of unhealthy food intake among girls and boys. Among boys, cell phone use and video game were also associated with the consumption of unhealthy foods. Overall, girls and boys with the higher daily frequency of screen use (≥3/day) showed higher unhealthy food intake.

Our findings were similar to those observed in a prospective study conducted with US children and adolescents aged 9–16 years in which, over 2 years, the increase in screen time was associated with increased consumption of foods with low nutritional quality and decreased consumption of fruits and vegetables². On the other hand, in a longitudinal study conducted with Danish children and adolescents (8–17 years old), an association between watching TV/ watching TV during meals and less healthy eating habits was observed in the first year of follow-up. This result was not maintained when analyzing the 6-year follow-up¹⁴.

Results from several cross-sectional studies underline the positive association between increased exposure to screens and consumption of unhealthy foods, although they make it impossible to establish a causal relationship¹²,¹³,²³. Another study found that screens helped children and adolescents to develop other unhealthy eating habits, such as skipping breakfast²⁴.

In Brazil, the prevalence of consumption of UPFs was 29.8% in ninth-grade adolescents (≥13 years) with up to 2 h of exposure to TV, video games, and computers and 42.8% among those with more than 2 h a day in front of these screens¹³. In a recent study carried out in Northeast Brazil, 2 h or more of exposure to TV, video games, and computers increased the chances of adolescents (12–14 years old) exhibiting a "Western" eating pattern, characterized by the consumption of foods with high energy density and poor in nutrients²³.

The association between exposure to screens and the consumption of unhealthy foods can be explained by the distraction caused by the use of computers and televisions, which causes a lack of awareness of the actual consumption of food and an automatic association of these behaviors with the simultaneous consumption of unhealthy foods²⁵. Furthermore, while watching or playing with cell phones, computers, or video games, children are often confronted with appearances of food, which affect their eating behavior²⁶, as well as impacting the preference for flavors such as sweet, greasy, and salty²⁷, a profile compatible with unhealthy foods.

Although watching TV is often associated with the consumption of unhealthy foods¹⁴, this was not observed in the current research. Currently, there is a reduction in the use of TV by Brazilian children, due to the prohibition of advertisements that encourage the consumption of unhealthy foods (Resolution No. 163/2014 of the National Council for the Rights of Children and Adolescents) (CONANDA)²⁸ which culminated in a reduction in the offer of children’s programs on open TV channels. On the other hand, there was an increase of 78.3% in the use of the internet as a means of entertainment in 2019, with the cell phone being the most used equipment to access it (98.6%)²⁹.

This longitudinal study evidenced the association between the daily frequency of screen use and the frequency of consumption of unhealthy foods, representing an advance in the analysis of the influence of each type of these behaviors on food consumption. This finding shows that the use of cell phones, computers, and video games may have greater importance in encouraging the consumption of unhealthy foods than TV among children and adolescents. Based on these results, efforts should be made to reduce the excessive use of screens through public policies, school health promotion programs, and the establishment of family norms for the use of screens among young people, as well as to avoid, whenever possible, the acquisition and consumption of unhealthy foods.

Although this study provides insights into the context of the association between screen use and food consumption, it has some limitations. First, food consumption was assessed based on reports of only 1 day at each follow-up point, without including weekend information, and thus it may not accurately reflect the usual behavior of the participants. New studies should consider the inclusion of reports of food consumption on at least 2 days, so that a more consistent assessment of this behavior is possible³⁰. Second, the exposures and outcome were self-reported and may have been influenced by memory and social desirability bias. However, previous validation studies of the Web-CAAFE showed that the instrument is valid and reliable for the assessment of food consumption¹⁷,¹⁸,³⁰. In addition, future studies could also verify the magnitude of the effect of the time of exposition to each screen on the consumption of unhealthy foods.

Finally, the study did not collect data on participants’ socioeconomic status, an important factor that can influence both food consumption and access to screen devices. However, our sample consisted only of students from a typical public school, intentionally selected for the Web-CAAFE validation study. The school is in a populous neighborhood, with strong and diverse commerce, but without individuals in extreme poverty. Thus, we believe that there were no substantial differences in the socioeconomic status of the participants at the time of data collection. Furthermore, in studies that analyzed the effect of economic level on the consumption of UPFs by Brazilian schoolchildren, based on data from the Brazilian National Survey of School Health (PeNSE), adolescents from higher socioeconomic levels³¹ and those enrolled in private schools³² ate more UPFs compared to their lower-income peers and those enrolled in public schools, respectively. Despite this, it is not clear whether socioeconomic status is an important factor in discriminating food consumption among public school students in Brazil.

The convenience sample limits generalizations, but the longitudinal analysis over the 1-year follow-up period was a strong point of the study, since both sedentary behaviors and food consumption are susceptible to seasonal variation³³,³⁴. Longitudinal analysis allows the identification of changes in behavior and the periods in which they occur and, based on these findings, specific interventions can be planned and developed, considering the most critical intervals in which the highest frequencies of unhealthy behaviors occur. Another strong point was the analysis of sedentary behaviors by type of screen because different sedentary behaviors can affect the consumption of unhealthy foods differently.

Based on the results, actions to promote healthy eating, together with restriction of exposure to screens, should be encouraged, with special attention to boys. A greater frequency of screen use led to greater consumption of unhealthy foods regardless of age, daily physical activity, BMI, and attendance in class. The use of video games, computers, and cell phones was positively associated with the consumption of unhealthy foods, with no effect of watching TV. Future research is needed to confirm the tested association, especially with longer follow-up periods, to help establish effective strategies to reduce exposure to screens and consumption of unhealthy foods.

• Funding:
  Fundação de Amparo à Pesquisa da Bahia [Edital nº 028/2012, Bolsa: PES0049 / 2013] and Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (processo CAPES: 88887.497290/2020-00).

ACKNOWLEDGMENTS

The authors would like to thank the Municipal Department of Education of Feira de Santana, the principal, teachers and parents.

Data availability statement:

The datasets generated and/or analyzed during the study are available from the corresponding author upon request.

How to cite:

Jesus GM, Araujo LDMS, Dias LA, Barros AKC, Amaral MVC, Assis MAA. Screen-based sedentary behaviors increase unhealthy food intake among children and adolescents. Cad. Saúde Colet., 2025;33(3):e33030044. https://doi.org/10.1590/1414-462X202533030044

REFERENCES

• ¹ Monteiro CA, Cannon G, Moubarac JC, Levy RB, Louzada MLC, Jaime PC. The UN Decade of Nutrition, the NOVA food classification and the trouble with ultra-processing. Public Health Nutr. 2018;21(1):5-17. https://doi.org/10.1017/s1368980017000234
  » https://doi.org/10.1017/s1368980017000234
• ² Falbe J, Willett WC, Rosner B, Gortmaker SL, Sonneville KR, Field AE. Longitudinal relations of television, electronic games, and digital versatile discs with changes in diet in adolescents. Am J Clin Nutr. 2014;100(4):1173-81. https://doi.org/10.3945/ajcn.114.088500
  » https://doi.org/10.3945/ajcn.114.088500
• ³ Leffa PS, Hoffman DJ, Rauber F, Sangalli CN, Valmórbida JL, Vitolo MR. Longitudinal associations between ultra-processed foods and blood lipids in childhood. Br J Nutr. 2020;124(3):341-8. https://doi.org/10.1017/s0007114520001233
  » https://doi.org/10.1017/s0007114520001233
• ⁴ Hancu A, Mihaltan F, Radulian G. Asthma and ultra-processed food. Maedica (Bucur). 2019;14(4):402-7. https://doi.org/10.26574/maedica.2019.14.4.402
  » https://doi.org/10.26574/maedica.2019.14.4.402
• ⁵ Coates AE, Hardman CA, Halford JCG, Christiansen P, Boyland EJ. Social media influencer marketing and children’s food intake: a randomized trial. Pediatrics. 2019;143(4):e20182554. https://doi.org/10.1542/peds.2018-2554
  » https://doi.org/10.1542/peds.2018-2554
• ⁶ Mitchell JA, Byun W. Sedentary behavior and health outcomes in children and adolescents. Am J Lifestyle Med. 2014;8(3):173-99. https://doi.org/10.1177/1559827613498700
  » https://doi.org/10.1177/1559827613498700
• ⁷ Carson V, Hunter S, Kuzik N, Gray CE, Poitras VJ, Chaput JP, et al. Systematic review of sedentary behaviour and health indicators in school-aged children and youth: an update. Appl Physiol Nutr Metab. 2016;41(6 Supl. 3):S240-65. https://doi.org/10.1139/apnm-2015-0630
  » https://doi.org/10.1139/apnm-2015-0630
• ⁸ 8. Stanczykiewicz B, Banik A, Knoll N, Keller J, Hohl DH, Rosińczuk J, et al. Sedentary behaviors and anxiety among children, adolescents and adults: a systematic review and meta-analysis. BMC Public Health. 2019;19(1):459. https://doi.org/10.1186/s12889-019-6715-3
  » https://doi.org/10.1186/s12889-019-6715-3
• ⁹ Zink J, Ebrahimian S, Belcher BR, Leventhal AM. Reciprocal associations between depression and screenbased sedentary behaviors in adolescents differ by depressive symptom dimension and screen-type. J Affect Disord. 2020;263:39-46. https://doi.org/10.1016/j.jad.2019.11.130
  » https://doi.org/10.1016/j.jad.2019.11.130
• ¹⁰ Elizabeth L, Machado P, Zinöcker M, Baker P, Lawrence M. Ultra-processed foods and health outcomes: a narrative review. Nutrients. 2020;12(7):1955. https://doi.org/10.3390/nu12071955
  » https://doi.org/10.3390/nu12071955
• ¹¹ 11. Rideout V, Robb M. The Common Sense census: Media use by tweens and teens [Internet]. Common Sense Media; 2019 [cited 2021 May 29]. Available from: https://www.commonsensemedia.org/sites/default/files/uploads/research/2019-census-8-to-18-key-findings-updated.pdf
  » https://www.commonsensemedia.org/sites/default/files/uploads/research/2019-census-8-to-18-key-findings-updated.pdf
• ¹² Ashdown-Franks G, Vancampfort D, Firth J, Smith L, Sabiston CM, Stubbs B, et al. Association of leisure-time sedentary behavior with fast food and carbonated soft drink consumption among 133,555 adolescents aged 12–15 years in 44 low- and middle-income countries. Int J Behav Nutr Phys Act. 2019;16(1):35. https://doi.org/10.1186/s12966-019-0796-3
  » https://doi.org/10.1186/s12966-019-0796-3
• ¹³ Costa CDS, Flores TR, Wendt A, Neves RG, Assunção MCF, Santos IS. Sedentary behavior and consumption of ultra-processed foods by Brazilian adolescents: Brazilian National School Health Survey (PeNSE), 2015. Cad Saúde Pública. 2018;34(3):e00021017. https://doi.org/10.1590/0102311x00021017
  » https://doi.org/10.1590/0102311x00021017
• ¹⁴ Hare-Bruun H, Nielsen BM, Kristensen PL, Møller NC, Togo P, Heitmann BL. Television viewing, food preferences, and food habits among children: A prospective epidemiological study. BMC Public Health. 2011;11:311. https://doi.org/10.1186/1471-2458-11-311
  » https://doi.org/10.1186/1471-2458-11-311
• ¹⁵ Oliveira JS, Barufaldi LA, Abreu G de A, Leal VS, Brunken GS, Vasconcelos SML, et al. ERICA: use of screens and consumption of meals and snacks by Brazilian adolescents. Rev Saúde Pública. 2016;50(Suppl. 1):7s. https://doi.org/10.1590/S01518-8787.2016050006680
  » https://doi.org/10.1590/S01518-8787.2016050006680
• ¹⁶ Instituto Brasileiro de Geografia e Estatística. Pesquisa nacional de saúde do escolar: 2015 [Internet]. Coordenação de População e Indicadores Sociais; 2016. Available from: https://biblioteca.ibge.gov.br/visualizacao/livros/liv97870.pdf
  » https://biblioteca.ibge.gov.br/visualizacao/livros/liv97870.pdf
• ¹⁷ Davies VF, Kupek E, de Assis MA, Natal S, Di Pietro PF, Baranowski T. Validation of a web-based questionnaire to assess the dietary intake of Brazilian children aged 7-10 years. J Hum Nutr Diet. 2015;28(Suppl. 1):93102. https://doi.org/10.1111/jhn.12262
  » https://doi.org/10.1111/jhn.12262
• ¹⁸ Jesus GM de, Assis MAA de, Kupek E. Validade e reprodutibilidade de questionário baseado na Internet (Web-CAAFE) para avaliação do consumo alimentar de escolares de 7 a 15 anos. Cad Saúde Pública. 2017;33(5):e00163016. https://doi.org/10.1590/0102-311X00163016
  » https://doi.org/10.1590/0102-311X00163016
• ¹⁹ Jesus GM, Assis MAA, Kupek E, Dias LA. Avaliação da atividade física de escolares com um questionário via internet. Rev Bras Med Esporte. 2016;22(4):261-6. https://doi.org/10.1590/1517869220162204157067
  » https://doi.org/10.1590/1517869220162204157067
• ²⁰ Ridley K, Ainsworth BE, Olds TS. Development of a compendium of energy expenditures for youth. Int J Behav Nutr Phys Act. 2008;5:45. https://doi.org/10.1186/1479-5868-5-45
  » https://doi.org/10.1186/1479-5868-5-45
• ²¹ Lohman T, Roche A, Martorell R. Anthropometric standardization reference manual. Champaign: Human Kinetics Books; 1988.
• ²² de Onis M. Development of a WHO growth reference for school-aged children and adolescents. Bull World Health Organ. 2007;85(9):660-7. https://doi.org/10.2471/blt.07.043497
  » https://doi.org/10.2471/blt.07.043497
• ²³ Neta ACPA, Steluti J, Ferreira FELL, Farias Junior JC, Marchioni DML. Padrões alimentares de adolescentes e fatores associados: estudo longitudinal sobre comportamento sedentário, atividade física, alimentação e saúde dos adolescentes. Ciênc Saúde Colet. 2021;26(Suppl. 2):3839-51. https://doi.org/10.1590/141381232021269.2.24922019
  » https://doi.org/10.1590/141381232021269.2.24922019
• ²⁴ Tambalis KD, Panagiotakos DB, Psarra G, Sidossis LS. Screen time and its effect on dietary habits and lifestyle among schoolchildren. Cent Eur J Public Health. 2020;28(4):260-6. https://doi.org/10.21101/cejph.a6097
  » https://doi.org/10.21101/cejph.a6097
• ²⁵ Boulos R, Vikre EK, Oppenheimer S, Chang H, Kanarek RB. ObesiTV: How television is influencing the obesity epidemic. Physiol Behav. 2012;107(1):146-53. https://doi.org/10.1016/j.physbeh.2012.05.022
  » https://doi.org/10.1016/j.physbeh.2012.05.022
• ²⁶ Villegas-Navas V, Montero-Simo MJ, Araque-Padilla RA. The effects of foods embedded in entertainment media on children’s food choices and food intake: a systematic review and meta-analyses. Nutrients. 2020;12(4):964. https://doi.org/10.3390/nu12040964
  » https://doi.org/10.3390/nu12040964
• ²⁷ Sina E, Buck C, Ahrens W, De Henauw S, Jilani H, Lissner L, et al. Digital media use in association with sensory taste preferences in European children and adolescents—results from the I.Family Study. Foods. 2021;10(2):377. https://doi.org/10.3390/foods10020377
  » https://doi.org/10.3390/foods10020377
• ²⁸ Conselho Nacional dos Direitos da Criança e do Adolescente (CONANDA). Resolução n^o 163, 13 de março de 2014 [Internet]. 2014 [cited Oct 6, 2025]. Available from: https://www.gov.br/mdh/pt-br/acesso-ainformacao/participacao-social/conselho-nacional-dos-direitos-da-crianca-e-do-adolescente-conanda/resolucoes/resolucao-163-_publicidade-infantil.pdf/@@download/file
  » https://www.gov.br/mdh/pt-br/acesso-ainformacao/participacao-social/conselho-nacional-dos-direitos-da-crianca-e-do-adolescente-conanda/resolucoes/resolucao-163-_publicidade-infantil.pdf/@@download/file
• ²⁹ Instituto Brasileiro de Geografia e Estatística. Pesquisa Nacional por Amostra de Domicílios Contínua 2018/2019 [Internet]. Brasil: Diretoria de Pesquisas, Coordenação de Trabalho e Rendimento; 2019 [cited 2021 Oct 14]. Available from: https://biblioteca.ibge.gov.br/visualizacao/livros/liv101794_informativo.pdf
  » https://biblioteca.ibge.gov.br/visualizacao/livros/liv101794_informativo.pdf
• ³⁰ Perazi FM, Kupek E, Assis MAA, Pereira LJ, Cezimbra VG, Oliveira MT, et al. Efeito do dia e do número de dias de aplicação na reprodutibilidade de um questionário de avaliação do consumo alimentar de escolares. Rev Bras Epidemiol. 2020;23:e200084. https://doi.org/10.1590/1980-549720200084
  » https://doi.org/10.1590/1980-549720200084
• ³¹ Costa BGG, Duca GFD, Silva KS, Benedet J, Malheiros LEA, Quadros EN, et al. Socioeconomic inequalities in the consumption of minimally processed and ultra-processed foods in Brazilian adolescents. Ciênc Saúde Colet. 2022;27(4):1469-76. https://doi.org/10.1590/1413-81232022274.03372021
  » https://doi.org/10.1590/1413-81232022274.03372021
• ³² Silva JB, Elias BC, Warkentin S, Mais LA, Konstantyner T. Factors associated with the consumption of ultra-processed food by Brazilian adolescents: National Survey of School Health, 2015. Rev Paul Pediatr. 2022;40:e2020362. https://doi.org/10.1590/1984-0462/2022/40/2020362
  » https://doi.org/10.1590/1984-0462/2022/40/2020362
• ³³ Jesus GM, Araujo LDMS, Dias LA, Barros AKC, Silva JS, Kupek E, et al. Variação sazonal das atividades físicas e sedentárias de estudantes no semiárido baiano. Rev Bras Ativ Fís Saúde. 2021;26:1-8. https://doi.org/10.12820/rbafs.26e0185
  » https://doi.org/10.12820/rbafs.26e0185
• ³⁴ Rossato SL, Olinto MTA, Henn RL, Moreira LB, Camey SA, Anjos LA, et al. Seasonal variation in food intake and the interaction effects of sex and age among adults in southern Brazil. Eur J Clin Nutr. 2015;69(9):101522. https://doi.org/10.1038/ejcn.2015.22
  » https://doi.org/10.1038/ejcn.2015.22

Edited by

• Responsible editor:
  Guilherme L Werneck https://orcid.org/0000-0003-1169-1436

Publication Dates

• Publication in this collection
  28 Nov 2025
• Date of issue
  2025

History

• Received
  31 Mar 2022
• Accepted
  30 Nov 2022