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Article

Sedentary Behaviors of a School Population in Brazil and Related Factors

1
Centro Universitario de Enfermería de Cruz Roja, Universidad de Sevilla, 41009 Sevilla, Spain
2
Departamento de Enfermagem, Universidade Federal do Pampa, Uruguaiana 1650, Bagé, Brazil
3
Departamento de Enfermagem, Universidade Federal Rio Grande do Sul (UFRGS), Porto Alegre 90040-000, Brazil
*
Author to whom correspondence should be addressed.
Int. J. Environ. Res. Public Health 2020, 17(19), 6966; https://doi.org/10.3390/ijerph17196966
Submission received: 12 August 2020 / Revised: 14 September 2020 / Accepted: 16 September 2020 / Published: 23 September 2020

Abstract

:
Background. Overweight and obesity arise from a complex range of genetic, environmental, behavioral, educational, and socioeconomic factors. The present study explored the sedentary practices and some life habits related to health among children from the school population of Uruguaiana (Rio Grande do Sul, Brazil). Methods: A cross-sectional descriptive study was conducted to characterize the life habits of the school boys and girls from fourth grade (9–10 years old). Results: A total of 470 fourth-grade boys and girls (9–10 years old) participated in the study. As regards the variables linked to habits, 24% of the boys and girls answered they had not had breakfast the day they completed the questionnaire and 51.8% stated they did not have breakfast any given day of the week. Regarding sedentary habits, 25.3% of children watched TV or played video games five or more hours a day and 9% rarely played sports with their parents or caregivers. Statistical significance was recorded between “number of hours watching TV and playing video games” and “playing sports with parents or caregivers” (p < 0.05). Conclusions: Association between the times spent watching TV or playing video games and the practice of physical exercise in the family proves once again the importance of the family in education for the health of children. The school provides direct access to schoolchildren and their parents to launch numerous health education programs.

1. Introduction

According to the World Health Organization, childhood obesity is a public health problem, whose prevalence has increased in recent years with more than 100 million affected in 2015 [1,2]. If current trends continue, in 2022 there will be more children with obesity than with underweight [3]. Overweight and obesity in children cause significant impacts on the physical and mental health of children, with a tendency toward obesity in adulthood and suffering from noncommunicable diseases such as diabetes and cardiovascular diseases [4,5]. In England, a third (33.5%) of children aged 10 years are classified as overweight or obese [6]. In Latin America, it is estimated that between 42.4 and 51.8 million children and adolescents are overweight or obese, representing 20 to 25% of the total population of children and adolescents [7]. Specifically, in Brazil, one-third of children in the population between 5 to 9 years old are overweight [8].
Overweight and obesity arise from a complex range of genetic, environmental, behavioral, educational, and socioeconomic factors. Poor eating habits and poor physical activity increase the probability that children will become obese adolescents [9]. The sedentary lifestyle, encouraged by the rapid evolution of technology, has been accentuated in recent years [10]. Life habits have been modified, especially in children who have adopted behaviors that predispose to minimal energy expenditure, close to a quiescent condition. Children spend many hours in front of television, playing video games, or managing social networks from their mobile phones [11]. According to a survey carried out in Brazil, entitled National Student Health Survey, the prevalence of adolescents exposed to at least two hours a day of television is 78% [12].
Individual or intrapersonal factors that may influence physical activity relate to individual characteristics and choices. This includes knowledge, skills, self-efficacy, and individual socioeconomic circumstance. Children living in residential homes have reported lower scores in perception of physical activity ability/success. Lack of money is a major barrier to involvement in leisure and social activities, with the most frequent activity being sport [13]. Very little is known about how various interpersonal processes and primary social groups (peers, social workers, family, and foster caregivers) that provide support and role modeling influence activity choices on participation for children. The presence of friends has a positive influence on the desire to engage in a particular activity more often [14]. Two studies have reported on institutional factors. These include the rules, regulations, policies, and structures of institutions like schools that constrain or promote certain behaviors such as physical activity [15].
There is scientific evidence highlighting the association between the number of hours that television is viewed and the omission of important meals of the day, such as breakfast, and also with the consumption of hypercaloric foods, high in sugars, carbohydrates, and saturated fats [5,16]. The omission of breakfast has also been linked to a higher body mass index and an increase in leisure time in sedentary activities [17]. One of the strategies to control obesity in Brazil is through the National Food and Nutrition Policy (PNAN), updated by Ordinance No. 2,715 /2011, which aims to improve the diet, nutrition, and health conditions of the Brazilian population, through actions in order to promote adequate and healthy eating practices, food, and nutritional surveillance, especially the prevention of health problems related to an inadequate diet [18].
Strategies to accelerate obesity prevention include modifying the child’s environment for healthy eating and physical activity [19]. For this, it is essential to analyze the characteristics and life habits of the children’s population. This study tried to know the sedentary practices and some life habits related to health among children from the school population of Uruguaiana (Rio Grande do Sul, Brazil), identifying the sociodemographic factors that can influence it. The main hypothesis of the study was that the exercise with the family reduces the hours of exposure to television and video games in children.

2. Materials and Methods

2.1. Study Design and Sample

A cross-sectional descriptive study was conducted to characterize the life habits of the school boys and girls from fourth grade (9–10 years old) in the city of Uruguaiana (Rio Grande do Sul, Brazil). Uruguaiana is a city of about 129,000 inhabitants, located at the western end of Rio Grande dos Sul. According to data from Brazilian Institute of Geography and Statistics, pediatric outpatient care is performed in hospitals and outpatient care is exclusively on demand. At present, no child health promotion services are offered, or child health monitoring services once the expectant mothers are discharged from the hospital after giving birth.
In order to obtain the sample, a random sampling was performed by clusters of the 24 urban public schools of the city. To calculate the minimum sample size, it was taken into account the number of schools, the number of children enrolled in the fourth grade, and the number of classes. There was a total of 810 children, 39 classes, and 26 schools. For a confidence level of 95% and an error range of 5%, the minimum size would be 264 children.

2.2. Data Collection and Statistical Analyses

A transcultural adaptation was conducted of the abridged questionnaire of Eating Habits of the School Population of the “PERSEO” program (Pilot School Reference Program for Health and Exercise against Obesity) by the Spanish Society of Community Nutrition, validated in the Spanish population and translated into Portuguese [20]. A draft was sent to three social scientists and experts in this area who were external to the research team. They performed a review of the translated questionnaire, considering aspects such as item comprehension (both questions and possible answers) in relation to the objectives of the research. The items related to sedentary habits were selected. Questions included single, multiple, and open answers that were grouped into different variables:
  • Sociodemographic variables: Age, gender, family situation, and the parents’ working situation and their types of occupation, according to the Brazilian classification of occupations [21].
  • Anthropometric variables: Weight, height, and body mass index.
  • Variables of sedentary habits: Screen time during lunch or dinner and sports activities with parents.
The questionnaire was applied to the selected sample in 12 schools. The headships of all the selected school centers were contacted. The data collection day and time were scheduled, and the boys’ and girls’ parents were asked to sign the informed consent. Two educated researchers, accompanied by the teacher of the group, explained the instructions to complete the questionnaire to the children and clarified any doubt. To record and analyze the data, a questionnaire was written in the specific tool from the EpiInfo application, Version 7.2.1.0. (Epi, Atlanta, GA, USA) This tool was also employed to perform the statistical analysis.
The frequency distributions of the main qualitative variables were calculated in the descriptive analysis, as well as the measurements of central tendency and dispersion of the quantitative variables. For the hypothesis contrast, the chi-square test or Fisher’s exact test and student’s t test were used, as it suited the case. The accepted statistical significance level for the hypothesis contrast was set at p < 0.05.
The researchers obtained documentary authorization from the Regional Coordinating Office for Education of Rio Grande do Sul and from the Research Ethics Committee of the University of Unipampa (Code: 05.2017). During the whole process of data collection, children’s anonymity was guaranteed and the ethical principles for medical research in human beings described in the latest revision of the Declaration of Helsinki were applied [22].

3. Results

A total of 470 fourth grade boys and girls (9–10 years old) participated in the study. Of the total participants, 46% were boys and 54% were girls and the mean age of the participating children was 9.8 years old (SD = 0.74). Thirty-eight percent were children from the morning group, 56.7% were from the afternoon group, and 5.3% were from the integrative group (morning and afternoon shifts). Of the children, 99.6% were Brazilian and 64.2% of the children stated they lived with their parents all the time, whereas the rest had different family situations.
The mean number of brothers/sisters reported by the children was 2.07, 83.5% of the children stated that their father worked, and 63.7% that their mother also worked. The main occupation of 51.4% of the mothers who worked was in the area of services and in retail sales in stores or supermarkets. Of the fathers, 77.1% shared this same occupation. The main sociodemographic and characteristics’ descriptive results of the sample are summarized in Table 1.
As regards the variables linked to habits, referred to in Table 2, 24% of the boys and girls answered they had not had breakfast the day they completed the questionnaire. Of those who had breakfast, 34.9% had taken milk or eaten legumes, dried fruits, or eggs; 24.4% fruits or natural fruit juices; 10.4% bread, rice, or potatoes; 6.9% cold meats and processed foods; and 3.1% candies, pastries, or salted snacks. With respect to the frequency of weekly breakfast, 51.8% stated they did not have breakfast any given day of the week, 49.3% had breakfast every day with their mother or father, and 14.2% never had breakfast with their mother/father. Regarding sedentary habits, 25.3% of children watched TV or played video games five or more hours a day, 55.3% watched TV at lunch and dinner, and 9.1% rarely played sports with their parents or caregivers.
The results related to the hypothesis contrast, obtained with the chi-square test, are presented in Table 3. The variable “screen time (hours)” became a dichotomous variable: Less than 2 h and 2 or more hours’ screen time. In comparison with other variables (sex, family situation, parents’ work, and breakfast habits), only statistical significance was recorded with two variables. There was a significant difference between screen time and sex (p < 0.05) and playing sports with parents or caregivers (p < 0.05). A multivariable regression model was made in order to identify the independent associations of screen time with physical activities controlling for age, sex, and sociodemographic variables. The result was not statistically significant (Table 4).

4. Discussion

In the present study, a descriptive analysis of the data was carried out and various sociodemographic hypotheses were tested, as well as dietary habits and sedentary habits compared to the number of hours they watched television and/or played video games. The National Student Health Survey, carried out in Brazil, showed that 78% of adolescents were exposed to at least two hours a day of television [12]. In our study, the population was school-aged between 9 and 10 years and the percentage of boys and girls who watched television or played video games at least 2 h a day was 55%.
Stahlmann et al. observed that children from single-parent families reported longer hours of screen time (p < 0.001) [23]. In our study, no significant difference was observed in this regard (p = 0.476). However, the highest percentage of boys and girls who had screen time over two hours was represented by those who lived with their father and a new partner (66.6%), followed by those who lived alone with their mother (46.7%).
There is scientific evidence linking the amount of screen time with the omission of important meals during the day such as breakfast [16]. Specifically, in our study, no significant differences were obtained between screen time and the weekly frequency of breakfast. This may be due to the fact that the problem in this population is more of education, habits, and family education than the influence of television. However, it was found that almost half of children who do not have breakfast had screen time of over two hours a day.
In this study, only 18% of boys and girls had breakfast every day of the week, a low rate compared to other studies carried out in Argentina [24], Spain [25], and Canada [26], where 75%, 77.5%, and 85.5%, respectively, had breakfast daily. However, in the study carried out in Spain [25], 5.2% had fresh juices or whole fruits for breakfast; on the contrary in our study, this breakfast (fresh juices or whole fruits) was taken by 24.4%. In the study carried out in Argentina, 16% had a low-quality breakfast, based on industrial pastries and sugary foodstuffs, while in Italy [27], 31.3% of children had this type of breakfast. By comparison, among Uruguaiana schoolchildren, only 3.1% had industrial pastries or sugary foodstuffs for breakfast. This was particularly noteworthy, so we can conclude that while there were fewer children who used to have the habit of a daily breakfast, those who did ate a healthier breakfast, mainly based on milk, bread, eggs, and fruit or fresh fruit juices. The problem in this case was the lack of the breakfast habit, which may be related to educational and socioeconomic factors due to scarcity of resources.
In previous studies, screen time was associated with a higher intake of “fast food”, foodstuffs and foods rich in saturated fat, refined sugar, food colors, and preservatives [28,29,30]. Overexposure to television and video games, in addition to leading to a sedentary lifestyle, exposes children to the advertising of unhealthy products [31,32]. In this study, no statistical significance was obtained between screen time and the type of breakfast that children had. This may be due to the fact that the advertising of processed products and fast food in Uruguaiana is less than in other cities. Some of the most recognized and publicized products by international celebrities are expensive and difficult to access for a population with limited resources.
Statistical significance was obtained between screen time and the frequency of practicing sports activities or physical exercise with their parents or other caregivers. This finding is similar to other ones found in previous studies, in which the important role of the family appears to be related to sedentary behaviors of children [33,34,35]. In fact, parents’ lifestyle habits are the main predictors of screen time by schoolchildren [36,37,38]. Bjelland et al. concluded that parental support and also their teaching and accompaniment in physical exercise decreased the screen time among European children [39,40,41,42]. A multivariate regression was performed between screen time and some sociodemographic variables and no statistical significance was obtained.
Another of the determining factors related to screen time is age and gender [43,44]. Regarding age, no differences were observed in this study, because there was hardly any variability, but on the other hand, there was a significant association between the screen time and gender. Even though the proportion of girls was lower, boys were more likely to have screen time during the day than girls, as 58.12% of them had screen time of less than two hours a day. According to Saunders et al., boys reported more screen time (two or more hours) than girls and total sedentary behavior [45]. Physical competence, motivation, and confidence were negatively associated with all modes of sedentary behavior. Knowledge and understanding about the benefit of physical exercise were negatively associated with screen-based modes of sedentary behavior and positively associated with nonscreen, sedentary behavior [46]. Public health interventions should continue to target screen-based sedentary behaviors, given their potentially harmful associations with important aspects of physical literacy [45].
Surveys in Brazil have shown that smoking, obesity, and unhealthy diets are more frequent in individuals with low educational attainment. The Brazilian phone surveillance system shows that leisure-time physical activity is most frequent in young adults, men, those with high educational attainment, and people who live near public spaces with equipment for physical activity. The progress in physical activity reflects improvements in active transportation, health education, communication strategies, and physical activity interventions funded by the Brazilian Ministry of Health. The Brazilian government launched a strategic plan to reduce exposure to risk factors, such as smoking, consumption of alcohol, physical inactivity, and salt intake. The government took account of the results of evaluation studies of community interventions to promote physical activity classes in community settings [47].
Most of the scientific community agrees that the growth in worldwide prevalence of obesity during the last decades is due to profound changes in our traditional lifestyle. Today people are more sedentary. Furthermore, many countries are losing their traditional diets by others precooked, sugary, and rich in animal fats’ products [48]. Several studies have shown the relationship between an increase of several sedentary behaviors, for instance, screen time, and weight gain [49]. A novel risk factor in children and adolescents is TV availability in the bedroom [50]. Interventions aimed at reducing screen time have been a focus of childhood obesity prevention and treatment. However, the evaluation of their effectiveness needs to be taken into account in order to develop successful prevention programs [51].
Although the technological revolution has been of great benefit to many populations throughout the world, it has come at a major cost in contribution of physical inactivity to the worldwide epidemic of noncommunicable disease. Evidence from Brazil suggests that although prevalence of physical inactivity increased greatly in people with low income, no significant differences were reported in those with higher earnings. As public health efforts to increase physical activity and decrease sedentary time proceed, standardized physical activity surveillance procedures need to be implemented broadly and repeatedly. These measures are necessary to understand which intervention strategies work and to identify populations at greatest risk [52].

Limitations

The intention of this study was to evaluate the sedentary behaviors of schoolchildren and their correlation with various sociodemographic and lifestyle factors. Children were asked to fill in the data related to their weight and height in order to determine their body mass index. There was a very low response rate because children did not know these data. The body mass index could have been compared to sedentary habits, if we had had a higher response rate. Therefore, as a proposal for improvement for the next study, it is recommended to personally size and weigh the children. It is also intended to include parents to expand the variables, as well as to contrast the information reported by the children.

5. Conclusions

In this study, an important finding was obtained, the association between the time spent watching television or playing video games and the practice of physical exercise in the family. Once again, the importance of the family in education for the health of children is highlighted. The school environment provides direct access to schoolchildren and their parents to launch numerous health education programs based on physical exercise and a healthy diet.

Author Contributions

J.A.P.-B., J.M.-L. and D.d.R.-V. participated in the conceptualization and design of the study and drafted the first version of it. R.R.-C., M.E.D.-V., and J.A.P.-B. participated in the development of the study and analysis of data. J.M.-L. and J.M.R.-R. revised critically the draft of the manuscript, with a key intellectual contribution to the final version. M.E.D.-V. and D.d.R.-V. participated in the data collection and, with J.A.P.-B. and R.R.-C., they made the final version of the manuscript. All authors have read and agreed to the published version of the manuscript.

Funding

This research received no external funding.

Acknowledgments

We express our sincere thanks to all participating schools and their respective teachers.

Conflicts of Interest

The authors declare no conflict of interest.

References

  1. WHO. Global Strategy on Diet, Phsysical Activity and Health. Available online: https://www.who.int/dietphysicalactivity/childhood/en/ (accessed on 17 September 2020).
  2. Lee, E.Y.; Yoon, K. Epidemic obesity in children and adolescents: Risk factors and prevention. Front. Med. 2018, 12, 658–666. [Google Scholar] [CrossRef] [PubMed]
  3. NCD Risk Factor Collaboration. Trends in adult body-mass index in 200 countries from 1975 to 2014: A pooled analysis of 1698 population-based measurement studies with 19.2 million participants. Lancet 2016, 387, 1377–1396. [Google Scholar] [CrossRef] [Green Version]
  4. Sahoo, K.; Sahoo, B.; Kumar, A.; Yasin, N.; Kumar, R.; Singh, A. Childhood obesity: Causes and consequences. J. Fam. Med. Prim. Care. 2015, 4, 187–192. [Google Scholar]
  5. Emond, J.A.; Longacre, M.R.; Drake, K.M.; Titus, L.J.; Hendricks, K.; MacKenzie, T.; Harris, J.L.; Carroll, J.E.; Cleveland, L.P.; Gaynor, K.; et al. Influence of child-targeted fast food TV advertising exposure on fast food intake: A longitudinal study of preschool-age children. Appetite 2019, 140, 134–141. [Google Scholar] [CrossRef]
  6. Wilkie, H.J.; Standage, M.; Gillison, F.B.; Cumming, S.P.; Katzmarzyk, P.T. Multiple lifestyle behaviours and overweight and obesity among children aged 9–11 years: Results from the UK site of the International Study of Childhood Obesity, Lifestyle and the Environment. BMJ Open 2016, 6, e010677. [Google Scholar] [CrossRef] [Green Version]
  7. Onis, M. Prevenção do sobrepeso e da obesidade infantis. J. Pediatr. 2015, 91, 105–107. [Google Scholar]
  8. Dias da Rosa-dos Santos, F.; Bueno-Vitola, C.; de Oliveira-Arriera, C.; da Silveira-Chagas, M.C.; Calcagno-Gomes, G.; Weiss-Pereira, F. Ações de enfermeiros e professores na prevenção e no combate à obesidade infantil. Rev. Rene. 2014, 15, 463–470. [Google Scholar]
  9. Borfe, L.; Rech, D.C.; Schulz-Benelli, T.E.; Nunes-Paiva, D.; Hedwing-Pohl, H.; Burgos, M.S. Associação entre a obesidade infantil e a capacidade cardiorrespiratória: Revisão sistemática. Rev. Bras. Promoção Saúde 2017, 30, 118–124. [Google Scholar] [CrossRef] [Green Version]
  10. Estratégias e ações na prevenção da obesidade em escolares. Available online: https://www.efdeportes.com/efd222/estrategias-e-acoes-na-prevencao-da-obesidade.htm (accessed on 21 September 2020).
  11. Pedroso-Dias, P.J.; Prado-Domingos, I.; Gonçalves-Ferreira, M.; Muraro, A.P.; Sichieri, R.; Veras-Gonçalves-Silva, R.M. Prevalence and factors associated with sedentary behavior in adolescents. Rev. Saúde Pública 2014, 48, 266–274. [Google Scholar]
  12. Guerra, P.H.; Farias-Junior, J.C.; Florindo, A.A. Comportamento sedentário em crianças e adolescentes brasileiros: Revisão sistemática. Rev. Saúde Pública. 2016, 50, 9. [Google Scholar] [CrossRef]
  13. Dowda, M.; Saunders, R.; Hastings, L. Physical activity and sedentary pursuits of children living in residential children’s homes. J. Phys. Act Health 2009, 6, 195–202. [Google Scholar] [PubMed]
  14. Hollingworth, K. Participation in social, leisure and informal learning activities among care leavers in England: Positive outcomes for educational participation. Child Fam. Soc. Work 2012, 17, 438–447. [Google Scholar]
  15. Gay, J.; Dowda, M.; Saunders, R. Environmental determinants of children’s physical activity in residential children’s homes. J. Phys. Act Health 2011, 8, 636–644. [Google Scholar] [PubMed] [Green Version]
  16. De Pinho, L.; Fagundes-Silveira, M.; de Carvalho-Botelho, A.C.; Prates-Caldeira, A. Identification of dietary patterns of adolescents attending public schools. J. Pediatr. 2014, 90, 267–272. [Google Scholar]
  17. Amigo-Vázquez, I.; Busto-Zapico, R.; Errasti-Pérez, J.M.; Peña-Suárez, E. Skipping breakfast, sedentarism and overweight in children. Psychol. Health Med. 2016, 21, 819–826. [Google Scholar]
  18. Ministério Da Saúde. Política Nacional de Alimentação e Nutrição; Secretaria de Atenção à Saúde. Departamento de Atenção Básica. Coordenação-Geral de Alimentação e Nutrição: Brasília, Brazil, 2013. [Google Scholar]
  19. Sliwa, S.A.; Sharma, S.; Dietz, W.H.; Dolan, P.R.; Nelson, M.E.; Newman, M.B.; Rockeymoore, M.; Economos, C.D. Healthy kids out of schools: Using mixed methods to develop principles for promoting healthy eating and physical activity in out-of-school settings in the United States. Prev. Chronic Dis. 2014, 11, 140207. [Google Scholar] [CrossRef]
  20. Zamora-Corrales, I.; Jensen, M.L.; Vandevijvere, S.; Ramírez-Zea, M.; Kroker-Lobos, M.F. Television food and beverage marketing to children in Costa Rica: Current state and policy implications. Public Health Nutr. 2019, 22, 2509–2520. [Google Scholar]
  21. Vereecken, C.; Dupuy, M.; Rasmussen, M. Breakfast consumption and its socio-demographic and lifestyle correlates in schoolchildren in 41 countries participating in the HBSC study. Int. J. Public Health 2009, 54, 180–190. [Google Scholar]
  22. World Medical Association. WMA Declaration of Helsinki-Ethical Principles for Medical Research Involving Human Subjects. Available online: https://www.wma.net/policies-post/wma-declaration-of-helsinki-ethical-principles-for-medical-research-involving-human-subjects/ (accessed on 17 September 2020).
  23. Stahlmann, K.; Hebestreit, A.; DeHenauw, S.; Hunsberger, M.; Kaprio, J.; Lissner, L.; Molnár, D.; Ayala-Marín, A.M.; Reisch, L.A.; Russo, P.; et al. A cross-sectional study of obesogenic behaviours and family rules according to family structure in European children. Int. J. Behav. Nutr. Phys. Act. 2020, 17, 32. [Google Scholar] [CrossRef] [Green Version]
  24. Fugas, V.; Berta, E.; Walz, F.; Fortino, M.A.; Martinelli, M.I. Hábito y calidad del desayuno en alumnos de dos escuelas primarias públicas de la ciudad de Santa Fe. Arch. Argent. Pediatr. 2013, 111, 502–507. [Google Scholar]
  25. González-Rodríguez, A.; García-Padilla, F.M.; Martos-Cerezuela, I.; Silvano-Arranz, A.; Fernández-Lao, I. Proyecto ANDALIES: Consumo, oferta y promoción de la alimentación saludable en los centros de educación secundaria de Andalucía. Nutr. Hosp. 2015, 31, 1853–1862. [Google Scholar] [PubMed]
  26. Lillico, H.G.; Hammond, D.; Manske, S.; Murnaghan, D. The prevalence of eating behaviors among Canadian youth using cross-sectional school-based surveys. BMC Public Health 2014, 14, 323. [Google Scholar] [CrossRef] [PubMed]
  27. Nardone, P.; Lauria, L.; Buoncristiano, M.; Pizzi, E.; Galeone, D.; Spinelli, A. I comporamenti alimentari dei bambini della scuola primaria in Italia fotografati dal Sistema di sorveglianza nazionale okkio alla salute. Epidemiol. Prev. 2015, 39, 380–385. [Google Scholar] [PubMed]
  28. Lee, B.; Kim, H.; Lee, S.K.; Yoon, J.; Chung, S.J. Effects of exposure to television advertising for energy-dense/nutrient-poor food on children’s food intake and obesity in South Korea. Appetite 2014, 81, 305–311. [Google Scholar] [CrossRef] [PubMed]
  29. Mendes-Lipinski, J.; Romero-Martín, M.; Jiménez-Picón, N.; Lomas-Campos, M.M.; Romero-Castillo, R.; Ponce-Blandón, J. Breakfast habits among schoolchildren in the city of Uruguaiana, Brazil. J. Vis. Exp. 2020. [Google Scholar] [CrossRef]
  30. Potvin-Kent, M.; Pauzé, E. The effectiveness of self-regulation in limiting the advertising of unhealthy foods and beverages on children’s preferred websites in Canada. Public Health Nutr. 2018, 21, 1608–1617. [Google Scholar] [CrossRef] [Green Version]
  31. Ponce-Blandón, J.A.; Pabón-Carrasco, M.; Lomas-Campos, M.M. Análisis de contenido de la publicidad de productos alimenticios dirigidos a la población infantil. Gac. Sanit. 2017, 31, 180–186. [Google Scholar] [CrossRef]
  32. Dixon, H.; Niven, P.; Scully, M.; Wakefield, M. Food marketing with movie caracter toys: Effects on young children’s preferences for unhealthy and healthier fast food meals. Appetite 2017, 117, 342–350. [Google Scholar] [CrossRef]
  33. Salmon, J.; Timperio, A.; Telford, A.; Carver, A.; Crawford, D. Association of family environment with children’s television viewing and with low level of physical activitiy. Obes. Res. 2005, 13, 1939–1951. [Google Scholar] [CrossRef]
  34. Hennessy, E.; Hughes, S.O.; Goldberg, J.P.; Hyatt, R.R.; Economos, C.D. Parent-child interactions and objectively measured child physical activity: A cross-sectional study. Int. J. Behav. Nutr. Phys. Act. 2010, 7, 71. [Google Scholar] [CrossRef] [Green Version]
  35. Neshteruk, C.D.; Nezami, B.T.; Nino-Tapias, G.; Davison, K.K.; Ward, D.S. The influence of fathers on children’s physical activity: A review of the literature from 2009 to 2015. Prev. Med. 2017, 102, 12–19. [Google Scholar] [CrossRef]
  36. Petersen, T.L.; Moller, L.B.; Brond, J.C.; Jepsen, R.; Grontved, A. Association between parent and child physical activity: A systematic review. Int. J. Behav. Nutr. Phys. Act. 2020, 17, 67. [Google Scholar] [CrossRef] [PubMed]
  37. Rollo, S.; Gaston, A.; Prapavessis, H. Cognitive and motivational factors associated with sedentary behavior: A systematic review. AIMS Public Health 2016, 3, 956–984. [Google Scholar] [CrossRef] [PubMed]
  38. Pearce, M.S.; Basterfield, L.; Mann, K.D.; Parkinson, K.N.; Adamson, A.J.; Reilly, J.J. Early predictors of objectively measured physical activity and sedentary behaviour in 8–10 year old children: The Gateshead Millennium Study. PLoS ONE. 2012, 7, e37975. [Google Scholar] [CrossRef] [Green Version]
  39. Xu, C.; Quan, M.; Zhang, H.; Zhou, C.; Chen, P. Impact of parents’ physical activity on preschool children’s physical activity: A cross-sectional study. PeerJ 2018, 2, e4405. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  40. Bjelland, M.; Soenens, B.; Bere, E.; Kovács, É.; Lien, N.; Maes, L.; Manios, Y.; Moschonis, G. VeldeSJ: Associations between parental rules, style of communication and children’s screen time. BMC Public Health 2015, 15, 1002. [Google Scholar] [CrossRef] [Green Version]
  41. Pearson, N.; Salmon, J.; Crawford, D.; Campbell, K.; Timperio, A. Are parental concerns for child TV viewing associated with child TV viewing and the home sedentary environment? Int. J. Behav. Nutr. Phys. Act. 2011, 8, 102. [Google Scholar] [CrossRef] [Green Version]
  42. Butte, N.F.; Gregorich, S.E.; Tschann, J.M.; Penilla, C.; Pasch, L.A.; De Groat, C.L.; Flores, E.; Deardorff, J.; Greenspan, L.C.; Martínez, S.M. Longitudinal effects of parental, child and neighborhood factors on moderate-vigorous physical activity and sedentary time in Latino childre. Int. J. Behav. Nutr. Phys. Act. 2014, 11, 108. [Google Scholar] [CrossRef] [Green Version]
  43. Abbott, G.; Salmon, J.; Timperio, A.; Kneebone, K.; Hnatiuk, J.; Hesketh, K. Cross-sectional and longitudinal associations between parents’ and preschoolers’ physical activity and TV viewing. Obes. Res. Clin. Pract. 2013, 2, e113. [Google Scholar] [CrossRef]
  44. Janssen, I.; LeBlanc, A.G. Systematic review of the health benefits of physical activitiy and fitness in school-aged children and youth. Int. J. Behav. Nutr. Phys. Act. 2010, 7, 1–16. [Google Scholar] [CrossRef] [Green Version]
  45. Saunders, T.J.; MacDonald, D.J.; Copeland, J.L.; Longmuir, P.E.; Barnes, J.D.; Belanger, K. The relationship between sedentary beheviour and physical literacy in Canadian children: A cross-sectional analysis from the RBC-CAPL Learn to Play study. BMC Public Health 2018, 2, 1037. [Google Scholar] [CrossRef] [Green Version]
  46. Foley, L.; Jiang, Y.; Mhurchu, C.N.; Jull, A.; Prapavessis, H.; Rodgers, A.; Maddison, R. The effect of active video games by ethnicity, sex and fitness: Subgroup analysis from a randomised controlled trial. Int. J. Behav. Nutr. Phys. Act. 2014, 11, 46. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  47. Rimmer, J.H.; Marques, A.C. Leisure, and sporting activities in both inclusive and disability specific settings. The outcome of inclusive physical activity communities is a society that respects and values the rights of all to have equal access to physical activity. Lancet 2012, 380, 195–196. [Google Scholar]
  48. Rey-López, J.P.; Vicente-Rodríguez, G.; Biosca, M.; Moreno, L.A. Sedentary behavior and obesity development in children and adolescents. Nutr. Metab. Cardiovasc. Dis. 2008, 18, 242–251. [Google Scholar] [PubMed]
  49. Santaliestra-Pasías, A.M.; Mouratidou, T.; Verbestel, V.; Huybrechts, I.; Gottrand, F.; Le Donne, C. Food consumption and screen-based sedentary behaviours in European adolescents: The Helena study. Arch. Pediatrics Adolesc. Med. 2012, 166, 1010–1020. [Google Scholar]
  50. Delmas, C.; Platat, C.; Schweitzer, B.; Wagner, A.; Oujaa, M.; Simon, C. Association between television in bedroom and adiposity throughout adolescence. Obesity 2007, 15, 2495–2503. [Google Scholar]
  51. Santaliestra-Pasías, A.M.; Rey-López, J.P.; Moreno-Aznar, L.A. Obesity and sedentarism in children and adolescents: What should be done? Nutr. Hosp. 2013, 28, 99–104. [Google Scholar]
  52. Hallal, P.C.; Andersen, L.; Bull, F.C.; Guthold, R.; Haskell, W.; Ekefund, U. Global physical activity levels: Surveillance progress, pitfalls, and prospects. Lancet 2012, 380, 247–257. [Google Scholar] [PubMed]
Table 1. Sociodemographic and characteristics of the sample.
Table 1. Sociodemographic and characteristics of the sample.
Qualitative VariablesCategoriesn%95% CI
SexWomen23054.0(49.2–58.6)
Men19646.0(41.3–50.7)
ClassMorning class 17338.0(33.7–42.5)
Afternoon class25856.7(52.1–61.2)
Integral class245.3(3.6–7.7)
NationalityBrasil 46899.6(98.4–99.8)
Others (Argentina, Uruguay) 20.4(0.04–1.2)
Family living situationAlone with mother/father9621.9(18.7–25.1)
With mother/father and her/his new partner 2967(4.2–9.2)
With father and mother28064.2(59.6–68.5)
With other adults 317.1(5.0–9.9)
Yes, he works37583.5(79.8–86.6)
He doesn’t work306.7(4.7–9.4)
I don’t know/I haven’t contact with my father449.8(8.2–11.4)
Type of father’s occupation0 Armed forces, Police, fire and military10.2(–0.21–0.6)
1 Senior members of the government, managers of public interest organizations and companies and managers10.2(−0.21–0.6)
2 Science and arts professionals81.8(1.5–2.3)
3 Level technicians112.4(1–3.8)
4 Administrative service workers255.6(3–7.6)
5 Service workers, retail salespeople in stores and markets19944.2(39.6–48.8)
6 Agricultural, forestry, hunting and fishing workers00.0(0.0–0.0)
7 Industrial and service production workers61.3(0.3–2.4)
8 Maintenance and repair workers30.7(−0.1–1.4)
Mother workYes, she works28863.7(57.9–66.9)
She doesn’t work14431.8(27.7–36.3)
I don’t know/I haven’t contact with my mother204.4(2.8–6)
Type of mother’s occupation0 Armed forces, Police, fire and military102.2(0.8–3.6)
1 Senior members of the government, managers of public interest organizations and companies and managers30.7(0–1.4)
2 Science and arts professionals51.1(0–2.1)
3 Level technicians122.7(1–4.1)
4 Administrative service workers255.6(3–7.7)
5 Service workers, retail salespeople in stores and markets17538.9(34.4–434)
6 Agricultural, forestry, hunting and fishing workers184.0(2.2–5.8)
7 Industrial and service production workers419.1(6.4–11.8)
8 Maintenance and repair workers4910.9(8.0–13.8)
Table 2. Habits of the sample.
Table 2. Habits of the sample.
VariablesCategoriesn%95% CI
Breakfast today in the morningYes34276.0(72.5–79.5)
No10824.0(18.8–28.2)
Breakfast classificationSweets, pastries and salty snacks143.1(1.50–4.7)
Red, processed and could meats316.9(4.5–9.3)
Milk, lean meats, legumes, nuts, eggs15734.9(30.5–39.3)
Vegetables and fruits11024.4(20.5–28.4)
Bread, pasta, rice, potatoes4710.4(7.6–13.3)
Frequency of breakfast with mother or fatherEveryday22249.3(44.7–54.0)
4–6 days per week296.4(4.2–8.7)
1–3 days per week6614.7(11.4–17.9)
Less than once a week5011.1(8.2–14.0)
Never6414.2(10.9–17.5)
Breakfast frequencyEveryday8118.0(14.4–21.6)
5–6 days per week8118.0(14.4–21.6)
1–4 days per week316.9(4.5–9.2)
None23351.8(47.1–56.4)
Screen time per dayNone4710.0(7.3–12.7)
1 h/day14530.9(26.6–35.1)
2 h/day5611.9(8.9–14.8)
3 h/day449.4(6.7–12)
4 h/day398.3(5.8–10.8)
5 or more hours/day11925.3(21.4–29.3)
Screen time at lunch or dinner per weekEveryday26055.3(50.8–59.8)
4–6 days/week418.7(6.1–11.3)
1–3 days/week6413.6(10.5–16.7)
Less than 1 day/week8017.0(13.6–20.4)
Never00.0(0.0)
Sport frequencyYes, most days17437.0(32.6–41.4)
Sometimes13929.6(25.4–33.7)
Only on weekends347.2(4.9–9.6)
Rarely439.1(6.8–11.8)
Never00.0(0.0)
Table 3. Contingency table using the chi-squared test.
Table 3. Contingency table using the chi-squared test.
VariablesScreen Time (2 h/2 or More hours)Statistical Value Pearson’s Chi-SquaredLevel of Significance
SexMales12.531p = 0.028 *
97 (50.3%)
96 (49.7%)
Women
132 (58.1%)
95 (41.9%)
Family living situationAlone with the mother4.527p = 0.476
40 (53.3%)
35 (46.7%)
Alone with the father
14 (73.4%)
5 (26.3%)
With the mother and her new partner
11 (55%)
9 (45%)
With the father and his new partner
3 (33.3%)
6 (66.6%)
With father and mother
151 (54.5%)
126 (45.5%)
With other adults
17 (56.7%)
13 (43.3%)
Father workYes, he works1.964p = 0.580
198 (53.4%)
173 (46.6%)
He doesn’t work
17 (56.7%)
13 (43.3%)
I don’t know
13 (61.9%)
8 (38.1%)
I haven’t contact with my father
14 (66.6%)
7 (33.3%)
Mother workYes, she works3.977p = 0.264
163 (57.2%)
122 (42.8%)
She doesn’t work
70 (49%)
73 (51%)
I don’t know
7 (58.3%)
5 (41.7%)
I haven’t contact with my mother
4 (80%)
1 (20%)
Breakfast frequency with parentsEveryday7.131p = 0.129
124 (56.4%)
96 (43.6%)
4–6 days per week
22 (66.7%)
11 (33.3%)
1–3 days per week
38 (56.7%)
29 (43.3%)
Less than once a week
32 (61.5%)
20 (38.5%)
Never
27 (42.2%)
37 (57.8%)
Breakfast frequencyEveryday0.270p = 0.966
128 (53.8%)
110 (46.2%)
5–6 days/week
18(54.5%)
15 (45.5%)
1–4 days/week
46(56.8%)
35 (43.2%)
None
42(53.2%)
37 (46.8%)
Breakfast classificationSweets, pastries and salty snacks0.78p = 0.999
8(57.1%)
6(42.9%)
Red, processed and could meats
17(54.8%)
14(45.2%)
Milk, lean meats, legumes, nuts, eggs
87(55.4%)
70(44.6%)
Vegetables and fruits
62(56.4%)
48(43.6%)
Bread, pasta, rice, potatoes
28(57.1%)
21(42.9%)
Watch television at lunch or dinnerEveryday 1.118p = 0.773
147(56.5%)
113(43.5%)
4–6 days/week
23(56.1%)
18(43.9%)
1–3 days/week
31(49.2%)
32(50.8%)
Less than 1 day/week
44(55%)
36(45%)
Never
0(0%)
Sports per dayYes, most days35.208p = 0.019 *
96(55.5%)
77(44.5%)
Sometimes
85(61.6%)
53 (38.4%)
Only on weekends
17(50%)
17(50%)
Rarely
16(37.2%)
27(62.8%)
Never
31(52.5%)
28(47.5%)
* Indicates statistical significance.
Table 4. Multivariate regression model.
Table 4. Multivariate regression model.
ModelSum of SquaresdfMean SquareFStatistical Significance
Regression13,703527410.8220.535
Residual1,283,9693853335
Total1,297,673390
BetaSig.
Sex0.0620.225
Sport per day0.0470.351
Mother’s work0.0030.059
Father’s work−0.0760.145
Dependent variable: Screen time per day. Predictors: Sport per day, sex, mother’s work, father’s work.

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MDPI and ACS Style

Ponce-Blandón, J.A.; Deitos-Vasquez, M.E.; Romero-Castillo, R.; da Rosa-Viana, D.; Robles-Romero, J.M.; Mendes-Lipinski, J. Sedentary Behaviors of a School Population in Brazil and Related Factors. Int. J. Environ. Res. Public Health 2020, 17, 6966. https://doi.org/10.3390/ijerph17196966

AMA Style

Ponce-Blandón JA, Deitos-Vasquez ME, Romero-Castillo R, da Rosa-Viana D, Robles-Romero JM, Mendes-Lipinski J. Sedentary Behaviors of a School Population in Brazil and Related Factors. International Journal of Environmental Research and Public Health. 2020; 17(19):6966. https://doi.org/10.3390/ijerph17196966

Chicago/Turabian Style

Ponce-Blandón, José Antonio, María Eduarda Deitos-Vasquez, Rocío Romero-Castillo, Diogo da Rosa-Viana, José Miguel Robles-Romero, and Jussara Mendes-Lipinski. 2020. "Sedentary Behaviors of a School Population in Brazil and Related Factors" International Journal of Environmental Research and Public Health 17, no. 19: 6966. https://doi.org/10.3390/ijerph17196966

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