Artigos

Introduction
Physical activity (PA) and sedentary behavior (SB) have specific constructs and determinants1 despite their interrelationships2. For example, meeting PA recommendations does not imply relevant decreases in SB and screen time3,4. Although prolonged time in sedentary activities (e.g., commuting, school or work routines, and high exposure to screens) is prevalent across all age groups and contributes to increased sedentary time, it can be reallocated to more active behaviors3,5-7.
Promoting PA is recommended worldwide and the key objective of the Strategic Action Plan to Tackle Chronic Diseases and Noncommunicable Diseases in Brazil (2021-2030)8. However, the 2019 National School Health Survey (PeNSE) found that 53.1% of Brazilian adolescents spent more than three hours a day seated, 36% watched more than two hours of television (TV) daily, and only 28.1% were physically active. Several factors (e.g., transportation, financial difficulties, lack of support and public spaces, and the inability of parents to support the practice of PA) contribute to physical inactivity, and prolonged time in SB is associated with negative outcomes in school performance, social relationships, and food consumption9-13.
Despite the recommendations of the Brazilian Dietary Guidelines14 to reduce the consumption of ultra-processed foods, 75.4% of Brazilian adolescents reported excessive consumption associated with indicators of sedentary behavior (i.e., sitting > 4 h/day, watching TV > 3 h/day, and eating/studying in front of screens ? 4 days/week)15. Reducing screen time during meals and adopting protective behaviors (eating breakfast or eating meals with family or both) are associated with better diet quality16,17. Physical inactivity and excessive screen time are associated with poorer self-perception of health18, while adolescents who perceive themselves as overweight spend more time sitting and are less active19,20.
International data showed that approximately 80% of adolescents did not meet the PA recommendations21,22; gender inequality was also evident, with girls spending less time per week in physical education classes9,23. As prolonged sitting time is also an isolated risk factor for cardiovascular conditions in the general population and must be considered in younger individuals, recreational screen time for adolescents should not exceed 120 minutes per day24,25.
School-based PA plays a key role in increasing PA time. The Physical Activity Guide for the Brazilian Population recommends at least three 50-minute physical education classes per week, while the Bill of Law 3,467/201926 and Law of Guidelines and Bases of Education (9,394/1996)27 include sports practice in schools as a policy and right for Brazilian students. The right to PA is also guaranteed in Brazil by the Federal Constitution of 1988 and the Statute of Children and Adolescents of 199021. These documents ensure the right of health, information, culture, leisure, sports, and entertainment to the youth, emphasizing the duty of the state to promote formal and informal sports practices as an individual right28.
Despite this, previous studies indicated that more than 50% of adolescents were not engaged and spent less than 50 minutes per week in school-based PA. Thus, strengthening the school environment is essential to actively promote changes in the movement behavior of students1,9 while outlining the movement behavior profiles of Brazilian adolescents (e.g., high SB, high screen time, and low PA) and their impact on health outcomes may contribute to addressing this issue.
Therefore, this study aimed to identify the associations between movement behavior and lifestyle, food consumption, and health status (self-perception of health status and body image) of Brazilian adolescents.
Methods
Study design and data source
This study used data from the PeNSE 2019, which is an epidemiological study conducted by the Brazilian Institute of Geography and Statistics and approved by the research ethics committee of the Brazilian Ministry of Health (CAAE 3.249.268). The PeNSE provides nationwide data on Brazilian children and adolescents of both sexes enrolled in the 7th, 8th, or 9th grades of elementary school or the first and third years of high school (public and private)29-31.
Data were collected using mobile devices in a two-stage cluster sampling design: the first stage involved information from 4,242 public and private schools, while the second included 6,612 classes of students. A total of 159,245 questionnaires were applied, and 125,123 were included in the final sample31.
Inclusion criteria were adolescents aged between 13 and 17 years old who provided responses related to movement behavior, food consumption, lifestyle, and health status. The final sample included 99,486 adolescents.

Study variables
Exposure variable
Movement behavior
The movement behavior profiles of the adolescents were defined based on the interaction between accumulated PA (sum of the time spent on active commuting and PA practice inside and outside the school in the past seven days) and SB data (daily TV and screen time while sitting).
Movement behavior was assessed using questions about accumulated PA (“How much time do you spend walking or biking to school?”, “How much time do you spend walking or biking from school?”, and “How much time per day do you engage in PA or sports during physical education classes - excluding theoretical classroom activities?”) and PA outside physical education classes (“How much time per day did these activities last?”). Two categories were created based on previous PeNSE studies22,32: physically active (? 300 minutes of accumulated PA) and inactive or insufficiently active (< 300 minutes of accumulated PA).
SB was defined based on two questions about screen time and time spent in sedentary behavior per day: “How many hours per day do you watch TV (excluding Saturdays, Sundays, and holidays)?" and “How many hours per day do you usually spend sitting, watching TV, playing video games, using a computer, cell phone, tablet, or doing other activities while sitting (excluding Saturdays, Sundays, holidays, or time sitting at school)?”. According to PeNSE recommendations, more than two hours of TV and three hours of screen time per day were considered high SB31.
Two independent variables were created for movement behavior: one considering PA and TV time and another considering PA and screen time. For each variable, the following profiles were defined: physically inactive with high SB, physically inactive with low SB, physically active with high SB, and physically active with low SB. TV time and screen time were considered separately.


Dependent variables
Food consumption
Data on food consumption (natural, minimally processed, and ultra-processed foods) over the past seven days were analyzed. Questions about the consumption of beans, fresh fruits or fruit salads, and some types of vegetables or greens were used for natural and minimally processed food groups. Ultra-processed food consumption was assessed using questions about the intake of sweet treats (e.g., candies, chocolates, chewing gum, chocolates, lollipops, and soft drinks) and eating at the canteen, hot dog stands, pizzerias, and fast-food establishments33.
The group of natural and minimally processed foods was classified as healthy eating markers (HEM) and categorized as frequent (i.e., more than five servings in the last seven days) or infrequent consumption (i.e., up to four servings in the last seven days). Ultra-processed food products were classified as unhealthy eating markers (UEM) and also categorized as frequent (i.e., more than two times per week) or infrequent consumption (i.e., up to two times per week).

Lifestyle habits
The following questions about eating routine at home were used to collect data about lifestyle: “Do you usually have breakfast?” and “During meals, do you usually eat while doing something else (watching TV and using the computer or cell phone)?”. Responses from both questions were categorized as “up to twice a week” or “three or more times a week”.
Food consumption in the school environment was assessed based on questions related to the consumption of school meals and categorized as “yes” or “no”: “Do you usually eat food or snacks provided by the school (excluding the food bought at the canteen)?” and “Do you usually buy food or drinks at the canteen (excluding water)?”23.
Regarding social relationships, the question “How many close friends do you have?” was categorized as “up to one friend” and “two friends or more”. For alcohol consumption and tobacco use, the following variables were employed: “consumption of alcoholic beverages” and “tobacco use”; responses to both variables were categorized as “yes” and “no”.

Health status
Questions related to body perception (“How do you perceive your body?”) and self-perception of health status ("How would you classify your health status?") were used to assess health status. Responses to the former were categorized as “perceive to be obese” and “do not perceive to be obese”, whereas the latter was categorized as “very good/good” and “regular/negative/very negative”.

Adjustment variables
The following variables were used to adjust the model: sex (male or female), age (13 to 15 and 16 to 17 years), race or skin color (white, black/brown, or other), and maternal education (no education, complete or incomplete elementary education, complete or incomplete secondary education, and complete or incomplete higher education)31.
These variables were considered for their impact on access to spaces for PA, lifestyle, food consumption, and health status. Higher maternal education is associated with higher income and family education, positively influencing the encouragement of PA among adolescents, including access to spaces for PA during leisure time9. The PeNSE 2019 also showed greater physical inactivity in girls and those in the 16- to 17-year-old group9. Figure 1 presents the conceptual model, including dependent, independent, and adjustment variables.

Fig.1

Statistical analysis
The sample weight was applied, and the effect of the sample design was considered in the analysis due to the sampling complexity. Descriptive analyses and the prevalence of outcomes for each independent variable were calculated along with the respective 95% confidence intervals (95%CI). Pearson’s chi-square test was conducted to identify differences between the outcomes (eating routine at home, eating routine at school, close friends, alcohol consumption, tobacco use, HEM, UEM, self-perception of health status, and body perception) and categories of each independent variable.
A Poisson regression analysis estimated the raw prevalence ratio (PR) and 95%CI between outcomes and independent variables. Associations with p < 0.20 were included in the multivariate model. A second Poisson regression adjusted by sex, age, race or skin color, and maternal education was performed using a hierarchical model, with variables entered in ascending order of p-value; only those with p < 0.05 remained in the final model. Data analysis was conducted using the Stata software version 14 (StataCorp LLC, Texas, USA), and significance was set at p < 0.05.
Results
The final sample consisted of 50.2% male adolescents (65.5% aged between 13 and 15 years). A total of 58.1% of participants were black or brown, and 34.9% had mothers with elementary education. The most prevalent profile of adolescents in the accumulated PA + TV time outcome was physically inactive with low SB (46.6% [95%CI: 45.9 to 47.3]), while the most common profile for the accumulated PA + screen time outcome was physically inactive with high SB (38.9% [95%CI: 38.2 to 39.6]) (Table 1).
The results of the bivariate association between movement behavior profiles and food consumption, lifestyle, and health status of Brazilian adolescents are shown in the supplementary material.

Tab.1

Tab.2

Tab.3

Figure 2. Adjusted prevalence ratios between movement behavior variables (accumulated physical activity + TV time and/or accumulated physical activity + screen time) and food consumption, lifestyle, and health status among Brazilian adolescents. A. HEM consumption; B. UEM consumption; C. Have breakfast; D. Use screen during meals; E. Buy food at the canteen; F. Eat school meals; G. Consume alcoholic beverages; H. Tobacco use; I. Close Friends; J. Body image; K. Self-perception of health status.
Tables 2 and 3 demonstrate the raw PR between movement behavior variables and outcomes. The adjusted analysis for the accumulated PA + TV time outcome showed that the physically active profile with high SB had the highest prevalence of not perceiving themselves as obese (PR = 1.03). Regarding the movement behavior related to accumulated PA + screen time outcome, the results showed that the physically inactive profile with low SB had a low prevalence of UEM consumption three times or more per week (PR = 0.96), HEM consumption five times or more per week (PR = 0.97), buying food at the canteen (PR = 0.94), consumption of alcoholic beverages (PR = 0.94), and tobacco use (PR = 0.92). However, this group also had a high prevalence (PR = 1.11) of eating in front of a screen three or more times per week. The physically active profile with high SB presented a high prevalence of not perceiving themselves as obese (PR = 1.02), whereas the physically active profile with low SB had a low prevalence of buying food at the canteen (PR = 0.96) (Figure 2).

Discussion
This study evaluated the associations between movement behaviors and food consumption, lifestyle, and health status of Brazilian adolescents. Our findings suggest that even in adolescents not meeting the weekly PA recommendation, low screen time was associated with a low prevalence of HEM and UEM consumption per week and eating in front of screens. Additionally, the prevalence of alcohol consumption and tobacco use was low among physically inactive adolescents with low screen time. These findings underscore the relevance of limiting the screen time of Brazilian school students aged between 13 and 17 years with active behavior to improve food consumption. Furthermore, physically inactive and active adolescents with low screen time showed a low prevalence of buying food at the canteen, suggesting that reducing screen time improves food consumption in this population.
A Chinese study on the relationships between PA and SB found that 76% of students from preschool to high school were physically inactive and presented reduced sedentary time (based on total screen time per week)4. In our study, this characteristic was calculated based on TV time per week and observed in 46.6% of the sample. The authors34 also found corroborating results for the physically active group with low SB (15% to 20% of the sample). Although the physically active with a low SB profile is considered ideal for health, less than one-fifth of children and adolescents from the United Kingdom and China meet the criteria4,34.
Our results showed that physically inactive adolescents with high SB had a low prevalence of HEM and UEM consumption. The prevalence observed in those physically inactive with low screen time suggests an association between low SB and low HEM or UEM consumption regardless of PA. These findings also reinforce the notion that active behavior influences healthy behavior and improves quality of life, emphasizing the need to promote awareness among children and adolescents1. Additionally, despite the relationships with physical inactivity, a low SB was associated with a low frequency of buying food at the canteen and UEM consumption.
Chu et al.35 analyzed longitudinal data from 10,246 adolescents between 2016 and 2020 in the United Kingdom as part of the Adolescent Brain Cognitive Development study and observed that total screen time, social media use, and excessive screen time were associated with changes in eating patterns and symptoms of eating disorders.
In contrast, the low HEM consumption among physically inactive adolescents with low screen time may be attributed to the lack of nutritional education. While low SB may improve food consumption, the absence of nutritional education may decrease overall food consumption, including natural and minimally processed foods33.
The mealtime is a period for potential screen use during the day, and screens are often used as a babysitter to occupy children and adolescents at home12. Although strategies to reduce external SB in adolescents have been promoted, these habits persist at home and during meals despite their negative impacts on nutrition, including increased calorie consumption, reduced family mealtime participation, and less interest in meals containing healthy or unhealthy foods36. Likewise, the results of the present study indicated that adolescents with less screen time had low HEM and UEM food consumption.
Our study also found a low prevalence of consumption of alcoholic beverages and tobacco use in physically inactive adolescents with low screen time. According to the PeNSE 2015 (45.8%) and PeNSE 2019 (63.1%), the prevalence of alcohol consumption is high in Brazil and strongly associated with sociodemographic factors37,38.
Bianchi et al.39 showed that individual or group PA affects alcohol consumption differently. For example, living with only one guardian may decrease the likelihood of alcohol consumption, whereas higher income is associated with increased consumption of alcoholic beverages39. Data from the PeNSE 2015 indicated that increased time of PA was associated with an increased likelihood of alcohol consumption among male adolescents. Although PA is generally associated with improved health and reduced substance use, the consumption of alcoholic beverages is frequent among adolescents during the socialization phase35,40.
The results of the PeNSE 2019 and other studies31,40, such as those by the WHO41, in conjunction with the Brazilian Children and Adolescent Rights Act, highlight the relevance of combating tobacco use and provide reflections on the legislation and access to tobacco. Data from the PeNSE 2015 and 2019 indicated that approximately nine out of ten adolescents had purchased tobacco at least once, and seven out of ten regularly bought tobacco30,31. The present study showed that Brazilian adolescents with low TV or screen time presented a low prevalence of alcohol consumption and tobacco use regardless of PA, emphasizing that low SB may act as a protective factor against substance use.
Regarding body composition, Falbe et al.42 compared the assessments of SB with previous screen or TV time and found that increased screen time, especially TV, was associated with a high body mass index in young people. The study also indicated that TV was the media most associated with increased body mass index among adolescents aged 9 to 1942.
A longitudinal follow-up of adolescents in the United Kingdom identified that low PA combined with low, medium, or high sedentary time was associated with increased average body fat. In addition, longer TV time was progressively associated with increased body fat regardless of PA, highlighting the risk profile for adolescents linked to TV use34.
Our findings showed that physically active adolescents with high SB presented a low prevalence of perceived obesity, suggesting a protective factor even despite the high SB. Regular PA promotes well-being, improves quality of life, increases energy expenditure, and supports physiological and anatomical changes that help manage weight gain and obesity1.
Data from the PeNSE 2019 used in this study were obtained through self-reports using a mobile device, and adolescents self-reported their behaviors from the previous seven days; thus, recall bias is a potential limitation. Other possible limitations include the inability to specify PA levels or types and the potential misperception of self-reported screen time. Nevertheless, the use of recommended cutoff points for adolescents and the sample size helped adjust the biases in screen time and PA reporting. In addition, the use of questionnaires to assess PA provided a reliable framework for further investigation into PA levels, which are challenging to assess and analyze in adolescents40.
Other strengths of this study include the distinction between types of SB (TV or screen time) and the use of a nationwide population-based sample to investigate PA and SB and characterize the movement behavior of over 99,000 Brazilian adolescents aged between 13 and 17 years. Importantly, the present study also evaluated the associations of these movement behaviors with food consumption, lifestyle, and health status.
Our results indicate the need to improve the effectiveness of current legislation to promote health and prevent health issues in adolescents. As ensuring the rights of adolescents to quality physical education and access to PA environments may help reduce physical inactivity, health promotion actions should effectively regulate PA and focus on programs that reduce SB. Longitudinal studies are needed to better understand the relationships between PA, SB, food consumption, lifestyle, and health status.
In conclusion, SB was associated with variables within the domains of food consumption, lifestyle, and health status of Brazilian adolescents, regardless of the combined assessment of PA. Importantly, physically inactive adolescents with low screen and TV time presented a high prevalence of eating in front of screens, demonstrating the need to further explore the impacts of this factor on dietary changes of adolescents.

Collaborations
RA Bezerra took part in the study design, data acquisition, analysis and interpretation, as well as drafting and reviewing the manuscript and approving its final version. G Amorim contributed to the study design, analysis and interpretation of data, and took part in drafting and reviewing the manuscript and approving its final version. F Cheyenne took part in drafting and reviewing the manuscript and approved its final version. FV Cureau and I Ribeiro contributed to the study design, data analysis and interpretation, and took part in drafting and reviewing the manuscript and approving its final version.

Acknowledgments
To Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES) and Conselho Nacional de Pesquisa (CNPq) for their financial support.

Funding
This research was financed by the Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES) via a scholarship grant.

Data Availability Statement
The dataset for this article is available in the SciELO Data repository on the Ciência & Saúde Coletiva Dataverse at the link: https://doi.org/10.48331/SCIELODATA.EAOF24.


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