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Revista Brasileira de Cineantropometria & Desempenho Humano

Print version ISSN 1415-8426On-line version ISSN 1980-0037

Rev. bras. cineantropom. desempenho hum. vol.22  Florianópolis  2020  Epub May 18, 2020

https://doi.org/10.1590/1980-0037.2020v22e70170 

original article

What is the contribution of each physical activity domain to total physical activity in adolescents?

Qual a contribuição de cada domínio da atividade física para atividade física total em adolescentes?

Josiene de Oliveira Couto1 
http://orcid.org/0000-0002-1328-6859

Raphael Henrique Oliveira Araujo1 
http://orcid.org/0000-0002-9405-3052

Ellen Caroline Mendes da Silva1 
http://orcid.org/0000-0001-9856-0054

Nara Michelle Moura Soares1  2 
http://orcid.org/0000-0001-9910-1730

Antonio Evaldo dos Santos1 
http://orcid.org/0000-0001-8837-0329

Roberto Jerônimo dos Santos Silva1 
http://orcid.org/0000-0002-4578-7666

1Federal University of Sergipe. Graduate Program in Physical Education. São Cristóvão, SE. Brazil.

2Tiradentes University. Aracaju,SE. Brazil


Abstract

The present study aimed to verify the contribution of different physical activity domains to “total physical activity” in Brazilian adolescents. This is a cross-sectional study using secondary data from the third edition of the National School Health Survey - PeNSE, 2015. The sample consisted of 100,497 adolescents of both sexes enrolled in the 9th grade of elementary schools. A linear regression model was used to verify how much each domain contributed to total physical activity, considering gender, type of municipality and region. The domain with the largest contribution to “total physical activity” regardless of sociodemographic and environmental variables was “extra-school physical activity” (R2 = 0.60), followed by “active commuting” (R2 = 0.34), and finally “Physical Education classes” (R2 = 0.23). The contribution of the different domains varied by gender, type of municipality and region, and it was concluded that “extra-school physical activity” make the greatest contribution to “total physical activity”, followed by “active commuting” and “Physical Education classes”. In addition, variation was observed in the contribution of domains by gender, type of municipality and region.

Key words Adolescent behavior; Physical activity; Physical Education

Resumo

O presente estudo teve como objetivo verificar a contribuição dos diferentes domínios da atividade física na “atividade física total” em adolescentes brasileiros.Trata-se de estudo transversal utilizando dados secundários oriundos da terceira edição da Pesquisa Nacional de Saúde do Escolar – PeNSE, 2015. A amostra foi constituída por 100.497 adolescentes de ambos os sexos cursando o 9° ano do Ensino Fundamental. Utilizou-se um modelo de regressão linear a fim de averiguar o quanto cada domínio contribuiu para a atividade física total, considerando o sexo, tipo de município e região. O domínio que apresentou maior contribuição para “atividade física total” independentemente das variáveis sociodemográficas e ambientais foram as “atividades físicas extraescolares” (R2= 0,60), seguido do “deslocamento ativo” (R2= 0,34), e por fim as “aulas de Educação Física” (R2= 0,23). A contribuição dos diferentes domínios apresentou variações por sexo, tipo de município e região. Conclui-se que as “atividades extraescolares” apresentam a maior contribuição para a “atividade física total”, seguido do “deslocamento ativo” e das “aulas de Educação Física”. Além disso, verifica-se uma variação na contribuição dos domínios por sexo, tipo de município e região.

Palavras-chave Atividade física; Comportamento do adolescente; Educação física

INTRODUCTION

The regular practice of physical activity provides health improvements and acts preventing diseases at all stages of life1,2. In adolescence, the adoption of an active lifestyle can estimate the involvement in physical activity in adulthood3.

Despite this evidence, it appears that between 11 and 17 years of age, regardless of country, less than 20% of adolescents meet the recommendations of 60 daily minutes of physical activity4, however, when analyzing data from Brazil, a prevalence of 30.7% is estimated5.

Aiming at increasing the level of physical activity in adolescents, action plans were developed considering the different physical activity domains (leisure, work / school, domestic and commuting) and the context in which they are and can be developed4. In this sense, the different domains in which adolescents will engage may make different contributions to total physical activity.

In adolescence, actions and investigations have shown that the main focus is Physical Education classes6, extra-school physical activity7 and active commuting8, since there is greater engagement in these domains during this phase of life. However, it is observed that there is variation in the time spent among these domains9, indicating the need to verify the contribution of each of these in total physical activity in adolescents.

In this sense, understanding the contribution of the different domains that make up the total physical activity construct is essential for the development of interventions in which behavior change is one of the concerns, in a more responsive way, considering the needs of adolescents. In addition, there are few studies addressing these domains together during adolescence10.

Given the above, the present study aimed to verify the contribution of different physical activity domains to total physical activity in Brazilian adolescents.

METHOD

Study Design

This is a cross-sectional study using secondary data from the third edition of the National School Health Survey - PeNSE, 2015. This edition was developed between April and September 2015 in order to identify risk factors and health protection of Brazilian adolescents regularly attending school in the day shift11,12.

This study used data referring to the sample named “number one” of PeNSE, 2015. This sample consists of representative data of Brazilian adolescents enrolled in the 9th grade of elementary school in 201511,12. Fifty-three geographic strata were used, consisting of capitals and non-capital municipalities of each Federation Unit. In capitals, school and class sampling units were used; however, in non-capital municipalities, in addition to these, the IBGE agency was added11,12.

PeNSE 2015 was approved by the National Research Ethics Commitee - Conep No. 1.006.467, of 03/30/2015.

Participants

The survey was conducted with 3040 schools, 4159 classes and 102,301 students answered the survey questionnaire11. All students present in the selected classes were invited to participate in the research, but only those who agreed with the Free and Informed Consent Term, participated in the research11.

Instruments

Data were collected using an electronic questionnaire and the Personal Digital Assistant (PDA). To complete the questionnaire, students were instructed to consider the last seven days prior to the survey. Further information on PeNSE and its methodological aspects can be obtained from previous publication11,12.

Variables

For “active commuting”, the average daily time accumulated by the student was used, with commuting to and from school on foot or bicycle. For physical activities performed in “Physical Education classes”, the accumulated average duration in which the student practiced physical activity or sport during the period of Physical Education classes at school was used. For “extra-school physical activity”, the average daily time accumulated by the student with some physical activity performed during the extra-school period was considered. “Total physical activity” was estimated based on the product between the number of days and the average time spent by students in physical activities in commuting, physical education classes and extra-school physical activity domains, considering the seven days prior to the survey11.

Sociodemographic (gender, age and race) and environmental (type of municipality, regions and study shift) variables were used. The characterization and cutoff points of variables used are presented in Box 1.

Box 1 Characterization and cutoff points of variables used in this study 

Variables Categorization Criterion / Characterization
Gendera Female Biological Classification
Male
Age groupb ≤ 14 years Distribution Median
> 14 years
Race/Colorc Non white Race / Color: black, yellow, brown and indigenous
White All white
Regions Northern Federation Units groups
Northeastern
Southeastern
Southern
Mid-western
Type of Municipality Non capital Location
Capital
Study shiftd Part time Graded according to length of school stay "Part time" considered morning or afternoon; "Full time" only full time
Full time
Total Physical Activity Time Continuous Data In Minutes
Active commuting Continuous Data In Minutes
Physical activity in physical education classes Continuous Data In Minutes
Extra-School Physical Activity Continuous Data In Minutes

Note:

aQuestion VB01001;

bQuestion VB01003;

cQuestion VB01002;

dQuestion VB01022

Statistical analysis

For data interpretation, descriptive analysis and confidence interval (95% CI) were used. Linear regression model was used to verify how much each domain contributed to “total physical activity”, considering gender, type of municipality and region. Data are presented with their respective determination coefficient, beta estimator and confidence interval values, and throughout the analysis, 5% significance level (p ≤ 0.05) was considered. In all analyses, sample weights were used as weighting procedure considering instructions available for the PeNSE 2015 survey. Statistical treatment was performed using STATA version 15.0 software.

RESULTS

A total of 102,301 adolescents were interviewed, but 1.804 were excluded due to the lack of important information such as age and / or gender, in addition to data from those who did not attend the 9th grade of elementary school, resulting in a sample with 100,497 participants.

The sample consisted of adolescents of both sexes, mostly composed of females aged “≤ 14 years”, “non-white”, living in capitals and studying in a “part time” system (Table 1).

Table 1 Characteristics of sample participants. 

Variables N % 95% CI
Gender
Female 51.998 51.7 51.4 - 52.1
Male 48.499 48.3 47.9 - 48.6
Age group
≤ 14 years 68.097 67.8 67.5 - 68.0
> 14 years 32.400 32.2 32.0 - 32.5
Race/Color
Non white 67.034 66.8 66.5 - 67.1
White 33.359 33.2 32.9 – 33.5
Type of municipality
Non capital 50.055 49.8 49.5 - 50.1
Capital 50.442 50.2 49.9 - 50.5
Study shift
Part time 98.544 98.3 98.2 - 98.4
Full time 1.718 1.7 1.6 - 1.8

Note: 95% CI = confidence interval

The domain that made the greatest contribution to “total physical activity” was “extra-school physical activity”, followed by “active commuting” and “physical education classes”, respectively (Table 2).

.

Table 2 Contribution of different physical activity domains to total physical activity. 

Variables R2 P B CI (95%)
Extra-school physical activity 0.60 <0.001 1.12 1.11 - 1.13
Active commuting 0.34 <0.001 1.06 1.04 - 1.08
Physical education classes 0.23 <0.001 1.70 1.65 - 1.75

Note: R2 = determination coefficient; B = estimator B; 95% CI = confidence interval

Table 3 shows the contribution of “extra-school physical activity” to the total physical activity of adolescents. For female adolescents living in capitals, “total physical activity” was best explained by “extra-school physical activity” in the Mid-western region, with 56% (R² = 0.56), followed by the Southern region, with 54% (R² = 0.54). However, for those who do not live in capitals, this domain presented the largest contribution in the Southern region, with 60% (R² = 0.60), followed by the Northern region, with 53% (R² = 0.53).

Table 3 Contribution of “extra-school physical activity” to “total physical activity” in Brazilian adolescents, 2015 

Female
Capital Non capital
R2 p B CI (95%) R2 p B CI (95%)
Brazil 0.52 <0.001 1.07 1.05 - 1.09 0.52 <0.001 1.11 1.09 - 1.13
Northern 0.50 <0.001 1.06 1.02 - 1.10 0.53 <0.001 1.13 1.08 - 1.17
Northeastern 0.49 <0.001 1.06 1.03 - 1.09 0.47 <0.001 1.11 1.08 - 1.14
Southeastern 0.53 <0.001 1.05 1.01 - 1.09 0.52 <0.001 1.09 1.05 - 1.13
Southern 0.54 <0.001 1.08 1.02 - 1.13 0.60 <0.001 1.14 1.10 - 1.18
Mid-western 0.56 <0.001 1.13 1.08 - 1.17 0.50 <0.001 1.16 1.10 - 1.21
Male
Capital Non capital
R2 p B CI (95%) R2 p B CI (95%)
Brazil 0.63 <0.001 1.09 1.07 - 1.11 0.63 <0.001 1.12 1.10 - 1.14
Northern 0.64 <0.001 1.10 1.07 - 1.13 0.67 <0.001 1.13 1.09 - 1.16
Northeastern 0.66 <0.001 1.07 1.05 - 1.09 0.64 <0.001 1.12 1.09 - 1.14
Southeastern 0.61 <0.001 1.08 1.04 - 1.12 0.60 <0.001 1.11 1.06 - 1.15
Southern 0.67 <0.001 1.10 1.06 - 1.14 0.68 <0.001 1.15 1.11 - 1.19
Mid-western 0.63 <0.001 1.12 1.09 - 1.16 0.64 <0.001 1.15 1.11 - 1.18

Note: R2 = determination coefficient; B = estimator B; 95% CI = confidence interval

Regarding males, regardless of type of municipality, the largest contribution of “extra-school physical activity” to total physical activity occurred in the Southern region, with 67% (R² = 0.67) and 68% (R² = 0.68) for “capital” and “non-capital”, respectively.

Table 4 presents the contribution of “active commuting” to “total physical activity” in adolescents. For female adolescents living in capitals, the largest contribution of “active commuting” to “total physical activity” was found in the Northern region, with 42% (R² = 0.42), followed by the Northeast region, with 41% (R² = 0.41). However, regarding those who did not live in capitals, this contribution was higher in the Northeastern region, with 49% (R² = 0.49), followed by the Mid-western region, with 48% (R² = 0.48).

Table 4 Contribution of “active commuting” to “total physical activity” in Brazilian adolescents, 2015. 

Female
Capital Non capital
R2 p B CI (95%) R2 p B CI (95%)
Brazil 0.37 <0.001 0.96 0.93 - 0.99 0.42 <0.001 1.03 1.01 - 1.05
Northern 0.42 <0.001 1.00 0.95 - 1.05 0.46 <0.001 1.09 1.04 - 1.15
Northeastern 0.41 <0.001 0.96 0.92 - 0.99 0.49 <0.001 1.04 1.01 - 1.06
Southeastern 0.34 <0.001 0.94 0.89 – 1.00 0.38 <0.001 0.99 0.95 - 1.03
Southern 0.33 <0.001 0.96 0.86 - 1.06 0.35 <0.001 1.08 1.01 - 1.15
Mid-western 0.36 <0.001 1.04 0.97 - 1.11 0.48 <0.001 1.10 1.04 - 1.16
Male
Capital Non capital
R2 p B CI (95%) R2 p B CI (95%)
Brazil 0.31 <0.001 1.08 1.04 - 1.11 0.33 <0.001 1.13 1.08 - 1.17
Northern 0.31 <0.001 1.06 1.00 - 1.12 0.30 <0.001 1.16 1.09 - 1.23
Northeastern 0.26 <0.001 1.01 0.96 - 1.07 0.33 <0.001 1.14 1.09 - 1.19
Southeastern 0.34 <0.001 1.10 1.04 - 1.17 0.32 <0.001 1.09 1.00 - 1.17
Southern 0.30 <0.001 1.07 0.99 - 1.15 0.35 <0.001 1.20 1.12 - 1.28
Mid-western 0.31 <0.001 1.10 1.03 - 1.17 0.34 <0.001 1.13 1.05 - 1.21

Note: R2 = determination coefficient; B = estimator B; 95% CI = confidence interval

Considering male adolescents living in capitals, the contribution of “active commuting” to “total physical activity” was higher in the Southeast region, with 34% (R² = 0.34); however, for those who did not live in capitals, the largest contribution was in the Southern region, with 35% (R² = 0.35).

Table 5 presents the contribution of “Physical Education classes” to “total physical activity” in adolescents. It was found that for female adolescents, regardless of type of municipality, the largest contribution of “Physical Education classes” to “total physical activity” occurred in the Southern region, with 23% (R² = 0.23) and 24% (R² = 0.24) for “capital” and “non-capital”, respectively. Considering males living in capitals, the region in which “Physical Education classes” best explained “total physical activity” was the Mid-western region, with 25% (R² = 0.25); however, for those who do not live in capitals, this domain presented greater explanation in the Southeast region, with 26% (R² = 0.26).

Table 5 Contribution of “Physical Education classes” to “total physical activity” in Brazilian adolescents, 2015. 

Female
Capital Non capital
R2 p B CI (95%) R2 p B CI (95%)
Brazil 0.17 <0.001 1.43 1.36 - 1.51 0.19 <0.001 1.58 1.51 - 1.65
Northern 0.16 <0.001 1.53 1.38 - 1.69 0.18 <0.001 1.72 1.53 - 1.91
Northeastern 0.13 <0.001 1.35 1.25 - 1.45 0.15 <0.001 1.57 1.46 - 1.68
Southeastern 0.18 <0.001 1.43 1.30 - 1.57 0.19 <0.001 1.53 1.40 - 1.67
Southern 0.23 <0.001 1.64 1.40 - 1.88 0.24 <0.001 1.65 1.49 - 1.80
Mid-western 0.23 <0.001 1.59 1.45 - 1.74 0.23 <0.001 1.86 1.67 - 2.05
Male
Capital Non capital
R2 p B CI (95%) R2 p B CI (95%)
Brazil 0.19 <0.001 1.55 1.46 - 1.63 0.23 <0.001 1.69 1.60 - 1.78
Northern 0.17 <0.001 1.57 1.45 - 1.69 0.23 <0.001 1.69 1.57 - 1.80
Northeastern 0.18 <0.001 1.52 1.43 - 1.62 0.19 <0.001 1.53 1.43 - 1.63
Southeastern 0.18 <0.001 1.51 1.34 - 1.69 0.26 <0.001 1.74 1.55 - 1.92
Southern 0.15 <0.001 1.49 1.31 - 1.68 0.20 <0.001 1.75 1.56 - 1.94
Mid-western 0.25 <0.001 1.72 1.59 - 1.86 0.24 <0.001 1.80 1.62 - 1.97

Note: R2 = determination coefficient; B = estimator B; 95% CI = confidence interval

DISCUSSION

As the main result, it was found that the degree of contribution of physical activity domains to “total physical activity”, regardless of gender, type of municipality and region, were, respectively, “extra-school physical activity”, “active commuting” and “Physical Education classes”.

There is agreement between results of the present study and data obtained from PeNSE 2012, in which “extra-school physical activity” made the largest contribution to “total physical activity”, followed by “active commuting” and “Physical Education classes”10.

Regarding “extra-school physical activity”, it was found that for female adolescents living in capitals, this domain had the greatest contribution to “total physical activity” in the Midwestern region, however, for those who did not live in capitals, the greatest contribution occurred in the Southern region. For males, regardless of type of municipality, “extra-school physical activity” made the largest contribution to “total physical activity” in the Southern region.

One possible explanation for the greater engagement in “extra-school physical activity” in adolescents is their autonomy in choosing the activity to engage in13 and the social support offered, especially by friends, for involvement in extra-school physical activities14.

The result of the present study corroborates with previous study15 in which there was greater contribution of extra-school sports practice to “total physical activity”. Similarly, study9 found that during weekdays, adolescents had higher energy expenditure in sports practiced in the extra-school period. Study16 found that the practice of “extra-school physical activity” may be a strong predictor of subsequent practice over the years.

For female and male participants living in capitals, “active commuting” made the largest contribution to “total physical activity”, respectively, in the Northern and Southeastern regions. However, when considering those who do not live in capitals, this domain made the largest contribution to “total physical activity” in the Northeastern and Southern regions for females and males, respectively. These findings are compatible with previous study8, who identified the mode of “active commuting” to school as a correlate of levels of physical activity for both sexes. In contrast, Smith, Aggio, Hamer17 did not identify association between type of commuting and levels of physical activity.

One aspect that should be considered is that, depending on the region of the country, “active commuting” is a compulsory activity, in which sociodemographic and environmental factors can be considered determinant for its practice8,18.

Regarding “Physical Education classes” domain, it was found that adolescents of both sexes and living in capitals presented higher values ​​in the Mid-western region compared to the other regions, indicating greater contribution of this domain to the “total physical activity” accumulation. However, for those who do not live in capitals, the “Physical Education classes” domain made the largest contribution to “total physical activity” for females in the Mid-western region and for males in the Southeastern region. These findings can be explained by the variation in the number of Physical Education classes offered in the different regions, considering that regions with the highest economic development have the highest number of Physical Education classes and greater involvement in physical activity during this period19.

In this sense, study20 observed that the greater the offer of Physical Education classes, the greater the involvement of students in moderate to vigorous physical activities throughout the day. Similarly, previous studies have found that, on the days when Physical Education classes are offered, increase in levels of physical activities is observed, and the largest increase in these levels occurred mainly in inactive students19,21.

The limitation presented by this study is that the amount of practice in each physical activity domain was self-reported, thus, values ​​may be overestimated or underestimated and may not express the real values ​​of their practice. However, studies have been conducted using subjective methods and the methodological rigor adopted increased the reliability of information collected. As strength, the study has representative sample size and information obtained can be extrapolated, ensuring its internal and external validity.

CONCLUSION

It could be concluded that “extra-school physical activity” make the greatest contribution to “total physical activity”, followed by “active commuting” and “Physical Education classes”. In addition, variation was observed in the contribution of domains by gender, type of municipality and region.

Acknowledgments

We would like to thank the Brazilian Institute of Geography and Statistics for data collection, as well as CAPES for funding Couto, JO and Araujo, RHO.

COMPLIANCE WITH ETHICAL STANDARDS

FundingCoordination for Improvement of Higher Education Personnel (CAPES) agency.

Ethical approvalEthical approval was obtained from the local Human Research Ethics Committee – Conep and the protocol (no. 1.006.467) was written in accordance with the standards set by the Declaration of Helsinki.

How to cite this article

Couto JO, Araujo RHO, Silva ECM, Soares NMM, Santos AE, Silva RJS. What is the contribution of each physical activity domain to total physical activity in adolescents? Rev Bras Cineantropom Desempenho Hum 2020, 22:e70170. DOI: http://dx.doi.org/10.1590/1980-0037.2020v22e70170

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Received: November 21, 2019; Accepted: March 03, 2020

Corresponding author Roberto Jerônimo dos Santos Silva Graduate Program in Physical Education Department of Physical Education Federal University of Sergipe, São Cristóvão, Brazil.Av. Marechal Rondon s/n CEP: 49.100-000 Email: rjeronimoss@gmail.com

Conflict of interest statement

The authors have no conflict of interests to declare.

Author Contributions

Conceived and designed the study: JOC, RHOA, RJSS. Performed the experiments: JOC, RHOA, RJSS. Analyzed the data: JOC, RHOA, RJSS. Contributed reagents/materials/analysis tools: JOC, RHOA, ECMS, NMMS, AES, RJSS. Wrote the paper: JOC, RHOA, ECMS, NMMS, AES, RJSS.

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