INTRODUCTION

Sedentary behavior (SB) is characterized by activities performed in a sitting, reclining, or lying position and with energy expenditure less than or equal to 1.5 MET ¹ (metabolic equivalent). Each MET is equivalent to a basal O ₂

consumption of 3.5 ml/kg.min. Different types of SB include commuting, working in a sitting position, and a set of behaviors usually characterized as “screen time,” such as using the telephone and computer, watching television (TV), and playing video games ² .

Intense physical and psychological changes occur during adolescence, characterizing a relevant time for the study of SB ³ . Additionally, several SB markers have been shown to have an impact on health outcomes, such as obesity, arterial hypertension, hypercholesterolemia, low levels of self-esteem, social behavior problems, physical fitness, and academic performance ⁴ .

There are no national recommendations on the minimum time that should be spent on SB ⁵ . However, Brazilian studies ^6-7 often follow international recommendations, which suggest that adolescents limit this time to less than two hours per day. This cutoff point is well established for the behaviors of watching TV, playing video games, and using the computer. However, these devices do not fully represent the sedentary opportunities available to young people, since the use of mobile media, such as smartphones and tablets , is currently increasing among this population ⁸ . The Health Behavior in School-Age Children

(HBSC) report revealed that 56% to 65% of young people spent two or more hours per day watching TV. In turn, the National School-Based Health Survey (PeNSE) estimated a prevalence of 78% for this behavior. According to Barbosa et al. ⁹ , in 60% of the studies analyzed in their systematic review, the prevalence of excessive TV time was higher than 50% among Brazilian adolescents. According to Guerra et al. ²

, studies have demonstrated a positive association between increased screen time and low levels of physical activity (PA) and higher intake of high-calorie foods. However, the relationships with socioeconomic status and demographic factors, such as age and gender, are uncertain. These conflicting results can be justified by the heterogeneity of the cutoff points used to define excessive screen time, as well as by the assessment instruments used, and the study designs. In addition, there is a lack of studies that assess SB by exploring other indicators such as time spent using cell phones and tablets .

This is the first generation of adolescents raised entirely in the digital age, which has repercussions on a lifestyle centered on technological devices. Although the increase in access to such devices is evident, there is no consensus in the literature on which variables are associated with this type of SB. Therefore, it is necessary to contribute to the understanding of these associations and, from there, expand the evidence on the implications of exposure to these behaviors and encourage the development of preventive health interventions.

Thus, the objective of this study was to characterize four indicators of SB in adolescent students and verify their associations with sociodemographic, anthropometric and physical activity (PA) factors.

METHOD

An observational, analytical, cross-sectional study was carried out. The sample consisted of adolescents of both sexes, aged between 14 and 19 years, regularly enrolled in high school (1st, 2nd and 3rd years) in two public state schools and one private school in the municipality of Sombrio (Santa Catarina). According to the Brazilian Institute of Geography and Statistics (IBGE), the municipality has three public schools and one private school, and in 2015 it had 1,197 students enrolled in high school.

For the study, it was decided to include 10% of the enrolled students in the sample. The selection of public schools included in the study was carried out by random drawing. Students who were present in the classroom on the day of data collection were considered eligible. The exclusion criteria adopted were: being under 14 years of age and over 19 years of age and having mental and/or audiovisual limitations. Students who did not sign the assent form or did not hand in the informed consent form signed by their parents were considered as refusals. Those who did not attend the classroom on the day of collection were considered as losses.

Instruments

Information on sociodemographic aspects, SB and PA practice were extracted from a questionnaire developed in partnership with the Center for Research and Physical Activity and Health of the Federal University of Santa Catarina, based on the questionnaire Comportamentos dos Adolescentes Catarinenses (COMPAC) ¹⁰ . The SPHYNXR software ( Sphynx Software Solutions Incorporation, Washington, USA ) was used for optical reading of the questionnaires.

To measure the body mass of the participants, a digital scale ( Glass 200 G-Tech, Zhongshan, China ) was used, and height was determined by a portable stadiometer ( Sanny, São Paulo, Brazil ).

Procedures

Data collection took place from September to November 2016 with the participation of a team previously trained to apply the collection instruments. For the anthropometric assessment, the technical error of measurement (TEM) was used and a standard deviation of ±2 was considered acceptable. To this end, the researchers involved in data collection assessed 10 adolescents at two different times, with a minimum interval of seven days and a maximum of 15 days. The measurements of the most experienced evaluator were considered the gold standard.

Before the questionnaire was administered, the students were instructed to complete each section of the questionnaire. The application time was 40 to 50 minutes on average.

The sociodemographic variables analyzed in this study were: gender, age in complete years, father’s and mother’s education, asset index and type of school (public or private). The asset index was determined based on the methodology of the Brazilian Association of Research Companies (ABEP), which considers the presence of material assets and monthly employees in the household. It was calculated taking into account the different weights of the assets proposed by ABEP, with 0 being the minimum score and 87 the maximum possible score. The father’s and mother’s education level was determined by the question “Mark the alternative that best represents the level of education of your father and mother”, with the following response categories: never studied, did not complete elementary school, completed elementary school, did not complete high school, completed high school, did not complete college, completed college.

SB was operationalized based on four indicators (average daily time spent watching TV; playing video games and/or using the computer to play games; using the computer other than to play games and using a cell phone), assessed separately for weekdays and weekends. SB was defined as spending more than two hours performing each of these activities ² .

To measure the level of PA, the adolescents reported how often (times/week) and how long per day (hours/minutes) they practiced different activities of moderate to vigorous intensity. To characterize the adolescents as active and insufficiently active, the criteria of the World Health Organization (WHO) ¹¹ were used .

After completing the questionnaire, body mass and height were measured. Based on the measurements obtained, the body mass index (BMI) was calculated, which was categorized according to sex and age and expressed as a z-score of the WHO reference curve ¹² . The BMI was subsequently classified as “without excess weight” (normal weight, thinness and severe thinness) and “with excess weight” (overweight and obesity).

The study was approved by the Human Research Ethics Committee of the institution under number CAAE 66721517.2.0000.0121.

Statistical Analysis

For data analysis, descriptive statistics were initially performed, followed by bivariate analysis. Multivariate logistic regression was used to identify the independent factors associated with SB. Regression models were constructed that had as dependent variable each of the 4 indicators of SB during the week and weekends (binary categorical outcome: without SB – ≤ 2 hours/day and with SB – > 2 hours/day), and as exposure variables were considered: sex (female and male), father’s and mother’s education (incomplete elementary school; complete elementary school or incomplete high school; complete high school or complete or incomplete higher education), type of school (public or private), asset index (categorized in tertiles), BMI (without overweight and with overweight), and level of PA (insufficiently active and active). The results were expressed as Odds ratio (OR). Statistical analysis was performed using the SPSS ( Statistical Package for the Social Sciences ) version 17.0 statistical package.

RESULTS

Of the 175 potentially eligible adolescents, 104 participated in the study, whose mean age was 16.43 ± 0.98 years. As shown in Table 1, 63.5% were female, 82.7% studied in public school, 68.3% were insufficiently active, 17.2% were overweight or obese, and 26.4% of fathers and 45.3% of mothers had completed at least high school.

Figures 1 and 2 show the percentages of adolescents according to recommended screen time (≤2 hours/day) and non-recommended screen time (>2 hours/day) for TV use, games (on the computer and video games), computer use other than for gaming, and cell phone use during the week and on weekends. Higher percentages were observed related to cell phone and TV use during the week and on weekends. Time spent playing computer or video games was the behavior with the lowest percentages, both on weekdays and weekends.

Tables 2 and 3 present the results of the logistic regression for the association between the SB of watching TV and using a cell phone on weekdays and weekends, respectively, and the sociodemographic, anthropometric and PA level factors. In this regression, information related to 86 adolescents was used, due to the occurrence of missing data.

A statistically significant association was observed between sex and SB related to TV time (p = 0.034). Being female increased the chance (OR = 4.52; 95%CI = 1.12 – 18.24) of spending more than two hours watching TV on weekdays. In addition, an association was found between the BMI variable and SB related to cell phone use on weekdays. Individuals without excess weight had reduced chances of spending more than two hours using cell phones during the weekdays (OR = 0.19; 95%CI = 0.03 – 0.95) (Table 2).

SB related to the habit of watching TV on weekends was associated with BMI. Individuals who were not overweight had a lower chance of spending more than two hours watching TV (OR = 0.08; 95%CI = 0.01 – 0.45). Additionally, excessive time related to cell phone use on weekends was significantly associated with PA level. Adolescents with insufficient PA level were more likely to spend more than two hours adopting such behavior (OR = 3.42; 95% CI = 1.15 – 10.16) (Table 3).

No statistically significant associations (p > 0.05) were observed between the other types of SB on weekdays and weekends and the independent variables investigated in the present study.

DISCUSSION

The main results of the present study were: 1) Among the four SB indicators related to screen time, higher percentages were observed for cell phone use during the week and on weekends. 2) It was found that being female increased the chance of spending more time than recommended (> 2 hours/day) watching TV. 3) Not being overweight reduced the chances of spending excessive time using cell phones on weekdays. 4) On weekends, it was observed that not being overweight reduced the chances of spending excessive time watching TV, while insufficient PA increased the chances of adopting SB related to cell phone use.

Regarding the SB indicators related to screen time analyzed, the activity “cell phone use” stood out, reaching values ​​of 63.4% on weekdays and 60.6% on weekends. Although high rates were observed for this SB, these results are lower than those reported in a previous study, which reported a rate of 88.2% for this behavior ¹³ . The high prevalence of this SB among adolescents can be justified by the technological growth in Brazil. With the advent of the digital age, a new lifestyle has been taking shape and activities that were previously active have gradually been replaced by the use of tablets and cell phones, especially in large urban centers ⁹ .

The use of mobile devices, including cell phones, contributes significantly to reducing energy expenditure among adolescents and is among the main environmental factors determining obesity and overweight in young people ¹⁴ . Thus, the data collected in this study indicated that adolescents with an adequate BMI are less likely to spend more than two hours a week using cell phones. These findings reinforce those found by Kenney and Gortmaker ¹⁵ , who found that adolescents who spent more than five hours using screen devices ( smartphones, tablets , computers and/or video games), with the exception of TV, had a higher risk of developing obesity.

In the present study, female adolescents were more likely to spend more time than recommended watching TV on weekdays. Similarly, a study conducted in 56 public schools in Pelotas (RS) with a sample of 8,661 students found positive associations between female gender and TV time ¹⁶ . On the other hand, Lucena et al. ⁶ , demonstrated that male adolescents belonging to higher economic classes were more likely to spend more time watching TV than recommended. One of the reasons for this discrepancy may be the different cutoff points used to categorize SB related to TV use ² .

In this study, individuals who were not overweight were less likely to spend more than two hours watching TV on weekends. Similar results were found in the study by Júnior et al. ¹⁷ , carried out with adolescents of a similar age group, in which the habit of watching TV for two hours or more was shown to be associated with the prevalence of overweight and obesity. In contrast to these findings, Biddle et al. ¹⁸ concluded that the associations between SB and adiposity in adolescents are still weak and there is little or no evidence that this association is causal.

Silva et al. ¹⁹found a prevalence of insufficient PA levels above 50% in 8 of the 16 studies conducted with Brazilian adolescents, demonstrating the need to develop public policies focused on promoting healthy lifestyle habits in youth. At this stage of life, the frequent adoption of SB, such as cell phone use, increases the chances of presenting lower PA levels. Data found in the present study reinforce these statements, as students with insufficient PA levels were more likely to spend more than two hours using their cell phones on weekends. Similarly, a study conducted with American university students found a negative association between cell phone use and physical fitness. Individuals who used cell phones more frequently were more likely to forgo opportunities for physical activities and preferred to adopt more sedentary activities such as accessing Facebook and Twitter , surfing the internet, playing games, and using apps ¹³ . Although there are still few studies that separately assess the behavior of “cell phone use” and its influence on an individual’s PA level, it has been described in the literature that reducing the time spent on sedentary activities is a strategy to reduce physical inactivity ²⁰ .

Some limitations should be considered when interpreting the results of this study. First, the exposures and data presented were based on self-reported measures and the estimates may have been under- or overestimated. Most of the adolescents included in the sample are enrolled in public schools. The data were collected in a specific region of Brazil, and the students included in the sample may not represent the adolescent population as a whole. In addition, the absence of other statistical significance may have occurred due to the sample size assessed.

Finally, this study presented as a strength the measurement of SB related to screen time beyond the electronic devices commonly investigated (TV, video games and computers). Mobile media have been dominating different age groups and increasingly replacing old forms of entertainment and leisure. Therefore, the inclusion of cell phone use in this and future studies is important so that research can adapt to the current technological scenario.

CONCLUSION

Among the indicators of SB, cell phone use was the most frequent among adolescents, both on weekdays and weekends. In addition, cell phone and TV use were associated with modifiable aspects through health interventions, such as PA level and BMI. Therefore, the results obtained can contribute to the development of actions aimed at reducing this behavior among adolescents.

It is important to investigate the associations related to the use of mobile media devices, such as cell phones, since these have become an alternative platform for viewing content previously restricted to TV sets. Therefore, expanding the evidence regarding the use of these devices is necessary in order to recognize the impacts that they have on health and habits acquired during adolescence.

NOTE OF ACKNOWLEDGMENTS

We would like to thank the team that participated in the data collection, the adolescents, parents, the schools of the municipality of Sombrio, and the Center for Research and Physical Activity and Health of UFSC.

FINANCIAL SUPPORT

National Council for Scientific and Technological Development (CNPq), Call MCTI/CNPQ/UNIVERSAL 14/2014 – Process 456567/2014-3. Student Vanessa de Souza Vieira received a scholarship from the Institutional Program for Scholarships for Initiation in Scientific Research (PIBIC/CNPq). The student Susana da Costa Aguiar received a scholarship from the Coordination for the Improvement of Higher Education Personnel (CAPES) and the University Scholarship Program of Santa Catarina.