ABSTRACT

Objective:

To describe the prevalence of physical activity among subjects from birth cohorts of three cities located in different regions of Brazil according to sociodemographic characteristics and sex, comparing the relationships within and between cohorts.

Methods:

Cross-sectional study involving 12,724 adolescents and young adults who participated in five birth cohorts: Ribeirão Preto [1978/79 (37/39 years old in 2016) and 1994 (22 years in 2016)]; Pelotas [1982 (30 years in 2012) and 1993 (22 years in 2015)], and São Luís [1997/98 (18/19 years in 2016)]. Leisure-time physical activity was evaluated with questionnaires (insufficiently active: <150 min/week and active: ≥150 min/week) and moderate and vigorous physical activity (MVPA) was objectively measured by accelerometry. Those, in each city, were evaluated accordingly to skin color, socioeconomic classification, and study/work activities.

Results:

The prevalence of leisure-time physical activity ranged from 29.2% at 30 years old in Pelotas to 54.6% among adolescents from São Luís. The prevalence of leisure-time physical activity was higher among younger people (54.6% in São Luís 1997), while the same was not observed for total physical activity. MVPA (3^rd tercile) was higher in the cohorts from Pelotas and São Luís. The prevalence of leisure-time physical activity and MVPA was higher in men. The data showed that the variation in physical activity was associated with sex and sociodemographic conditions in all cohorts.

Conclusion:

Sociodemographic characteristics should be considered when promoting leisure-time physical activity and actions aimed at young people, and adults who are more socioeconomically vulnerable should be encouraged.

Keywords:
Physical activity; Descriptive epidemiology; Cohort studies; Accelerometry; Questionnaires

RESUMO

Objetivo:

Descrever a prevalência de atividade física entre sujeitos de coortes de nascimento de três cidades localizadas em diferentes regiões do Brasil segundo características sociodemográficas e sexo, comparando relações intra e intercoortes.

Métodos:

Estudo transversal com 12.724 adolescentes e adultos jovens que participaram de cinco coortes de nascimento: Ribeirão Preto [1978/79 (37/39 anos em 2016) e 1994 (22 anos em 2016)]; Pelotas [1982 (30 anos em 2012) e 1993 (22 anos em 2015)] e São Luís [1997/98 (18/19 anos em 2016)]. A atividade física no lazer foi avaliada com questionários (insuficientemente ativo: <150 min/semana; ativo: ≥150 min/semana) e a atividade física moderada e vigorosa (AFMV) foi medida objetivamente por acelerometria. Foram avaliadas a cor da pele, a classificação socioeconômica e as atividades de estudo/trabalho.

Resultados:

A prevalência de ativos no lazer variou de 29,2% aos 30 anos em Pelotas a 54,6% entre os adolescentes de São Luís. A prevalência de ativos no lazer foi maior entre os mais jovens (54,6% em São Luís/1997), o que não foi observado para a atividade física total. A AFMV (3^o tercil) foi maior nas coortes de Pelotas e São Luís. A prevalência de ativos no lazer e a AFMV foi maior nos homens. Os dados mostraram que a variação da atividade física foi associada ao sexo e às condições sociodemográficas em todas as coortes.

Conclusão:

As características sociodemográficas devem ser consideradas na promoção da atividade física no lazer e as ações voltadas para jovens e adultos mais vulneráveis socioeconomicamente devem ser incentivadas.

Palavras-chave:
Atividade física; Epidemiologia descritiva; Estudos de coortes; Acelerometria; Questionário

INTRODUCTION

The benefits of physical activity for the quality of life and health promotion have been extensively reported in recent decades¹1. Warburton DER, Bredin SSD. Health benefits of physical activity: a systematic review of current systematic reviews. Curr Opin Cardiol 2017; 32(5): 541-56. https://doi.org/10.1097/HCO.0000000000000437
https://doi.org/10.1097/HCO.000000000000... . Nevertheless, the global prevalence of physically active individuals continues to be low²2. Guthold R, Stevens GA, Riley LM, Bull FC. Worldwide trends in insufficient physical activity from 2001 to 2016: a pooled analysis of 358 population-based surveys with 1·9 million participants. Lancet Glob Health. 2018; 6(10): e1077-e1086. https://doi.org/10.1016/S2214-109X(18)30357-7
https://doi.org/10.1016/S2214-109X(18)30... and one-fourth of the world population does not reach the weekly minimum of 150 minutes of moderate physical activity or 75 minutes of vigorous activity recommended by the World Health Organization (WHO)³3. Bull FC, Al-Ansari SS, Biddle S, Borodulin K, Buman MP, Cardon G, et al. World Health Organization 2020 guidelines on physical activity and sedentary behaviour. Br J Sports Med. 2020; 54(24): 1451-62. https://doi.org/10.1136/bjsports-2020-102955
https://doi.org/10.1136/bjsports-2020-10... . The frequency of physical activity varies according to the location and economic situation of the country, with the lowest prevalence rates being observed in Latin America and high-income western and Asian countries. Among Latin American countries, Brazil has the lowest percentage of active people older than 18 years²2. Guthold R, Stevens GA, Riley LM, Bull FC. Worldwide trends in insufficient physical activity from 2001 to 2016: a pooled analysis of 358 population-based surveys with 1·9 million participants. Lancet Glob Health. 2018; 6(10): e1077-e1086. https://doi.org/10.1016/S2214-109X(18)30357-7
https://doi.org/10.1016/S2214-109X(18)30... .

Given the large territory and cultural and sociodemographic differences, the prevalence of physical activity varies widely among the different regions of Brazil. According to the Surveillance of Risk and Protective Factors for Chronic Diseases by Telephone Survey, the prevalence of leisure-time physical activity among all capital cities ranged from 34.6 to 49.9%⁴4. Brasil. Ministério da Saúde. Secretaria de Vigilância em Saúde. Departamento de Análise em Saúde e Vigilância de Doenças não Transmissíveis. Vigitel Brasil 2018: vigilância de fatores de risco e proteção para doenças crônicas por inquerito telefônico: estimativas sobre frequência e distribuição sociodemográfica de fatores de risco e proteção para doenças crônicas nas capitais dos 26 estados brasileiros e no Distrito Federal em 2018. Brasília: Ministério da Saúde; 2019.. Despite this variation, studies have consistently reported a higher prevalence of leisure-time physical activity among men, younger people, individuals with a higher educational level, and high socioeconomic groups⁵5. Pitanga FJG, Almeida MCC, Queiroz CO, Aquino EML, Matos SMA. Atividade física no Brasil: lições do Elsa-Brasil. Revisão narrativa. Sao Paulo Med J 2017; 135(4): 391-5. https://doi.org/10.1590/1516-3180.2017.0023190317
https://doi.org/10.1590/1516-3180.2017.0... ,⁶6. Wendt A, Ricardo LIC, Costa CS, Knuth AG, Tenório MCM, Crochemore-Silva I. Socioeconomic and gender inequalities in leisure-time physical activity and access to public policies in Brazil from 2013 to 2019. J Phys Act Health 2021; 18(12): 1503-10. https://doi.org/10.1123/jpah.2021-0291
https://doi.org/10.1123/jpah.2021-0291... . However, these associations are not well established when other physical activity domains are considered. The study⁷7. Hallal PC, Victora CG, Wells JCK, Lima RC. Physical inactivity: prevalence and associated variables in Brazilian adults. Med Sci Sports Exerc 2003; 35(11): 1894-900. https://doi.org/10.1249/01.MSS.0000093615.33774.0E
https://doi.org/10.1249/01.MSS.000009361... found no difference between genders for the set of leisure-time, work-related, domestic, and commuting-related physical activity, but observed lower physical activity in higher socioeconomic classes.

In addition to the importance of considering different physical activity domains, researchers highlight the need for methodological standardization of the measurements⁸8. Guthold R, Stevens GA, Riley LM, Bull FC. Global trends in insufficient physical activity among adolescents: a pooled analysis of 298 population-based surveys with 1·6 million participants. Lancet Child Adolesc Health 2020; 4(1): 23-35. https://doi.org/10.1016/S2352-4642(19)30323-2
https://doi.org/10.1016/S2352-4642(19)30... . Historically, population studies have used subjective instruments, especially validated questionnaires, because of their low cost and large-scale applicability⁹9. Ainsworth B, Cahalin L, Buman M, Ross R. The current state of physical activity assessment tools. Prog Cardiovasc Dis 2015; 57(4): 387-95. https://doi.org/10.1016/j.pcad.2014.10.005
https://doi.org/10.1016/j.pcad.2014.10.0... . In addition to subjective measures, accelerometry has been increasingly used in recent decades for the objective measurement of physical activity in population surveys¹⁰10. Knuth AG, Assunção MCF, Gonçalves H, Menezes AMB, Santos IS, Barros AJD, et al. Methodological description of accelerometry for measuring physical activity in the 1993 and 2004 Pelotas (Brazil) birth cohorts. Cad Saude Publica 2013; 29(3): 557-65. https://doi.org/10.1590/s0102-311x2013000300013
https://doi.org/10.1590/s0102-311x201300... . However, the adoption of different protocols and procedures of accelerometry has impaired the comparison of data between studies²2. Guthold R, Stevens GA, Riley LM, Bull FC. Worldwide trends in insufficient physical activity from 2001 to 2016: a pooled analysis of 358 population-based surveys with 1·9 million participants. Lancet Glob Health. 2018; 6(10): e1077-e1086. https://doi.org/10.1016/S2214-109X(18)30357-7
https://doi.org/10.1016/S2214-109X(18)30... ,¹¹11. Sasaki J, Coutinho A, Santos C, Bertuol C, Minatto G, Berria J, et al. Orientações para utilização de acelerômetros no Brasil. Rev Bras Ativ Fís Saúde 2017; 22(2): 110-26. https://doi.org/10.12820/rbafs.v.22n2p110-126
https://doi.org/10.12820/rbafs.v.22n2p11... ,¹²12. Ricardo LIC, Wendt A, Galliano LM, Muller WA, Cruz GIN, Wehrmeister F, et al. Number of days required to estimate physical activity constructs objectively measured in different age groups: findings from three Brazilian (Pelotas) population-based birth cohorts. PLoS One 2020; 15(1): e0216017. https://doi.org/10.1371/journal.pone.0216017
https://doi.org/10.1371/journal.pone.021... .

In 2014, the consortium of Brazilian birth cohorts from Ribeirão Preto(RP), Pelotas(PEL), and São Luís(SL) [RPS Consortium]¹³13. Confortin SC, Ribeiro MRC, Barros AJD, Menezes AMB, Horta BL, Victora CG, et al. RPS Brazilian birth cohorts consortium (Ribeirão Preto, Pelotas and São Luís): history, objectives and methods. Cad Saúde Pública 2021; 37(4): e00093320. https://doi.org/10.1590/0102-311X00093320
https://doi.org/10.1590/0102-311X0009332... was started, which enabled the comparability of different social, biological, and behavioral outcomes¹⁴14. Orellana JDY, Ribeiro MRC, Barbieri MA, Saraiva MC, Cardoso VC, Bettiol H, et al. Transtornos mentais em adolescentes, jovens e adultos do Consórcio de Coortes de Nascimento brasileiras RPS (Ribeirão Preto, Pelotas e São Luís). Cad Saúde Pública 2020; 36(2): e00154319. https://doi.org/10.1590/0102-311X00154319
https://doi.org/10.1590/0102-311X0015431... ,¹⁵15. Carvalho CA, Silva AAM, Victora C, Goldani M, Bettiol H, Thomaz EBAF, et al. Changes in infant and neonatal mortality and associated factors in eight cohorts from three brazilian cities. Sci Rep 2020; 10(1): 3249. https://doi.org/10.1038/s41598-020-59910-7
https://doi.org/10.1038/s41598-020-59910... ,¹⁶16. Mola CL, Cardoso VC, Batista R, Gonçalves H, Saraiva MCP, Menezes AMB, et al. Maternal pregnancy smoking in three Brazilian cities: trends and differences according to education, income, and age. Int J Public Health. 2020; 65(2): 207-15. https://doi.org/10.1007/s00038-019-01328-8
https://doi.org/10.1007/s00038-019-01328... among research centers located in different regions of Brazil with contrasting socioeconomic and demographic characteristics through the use of methodological standardization. Within this context and considering the variation in the prevalence of physical activity between different regions of Brazil and the difficulty in comparing data of different physical activity domains, this study aimed to determine the prevalence of leisure-time physical activity and objectively measured moderate and vigorous physical activity in adolescents and adults.

METHODS

The present data were obtained from recent follow-ups of five birth cohort studies conducted in 1978/79 (follow-up at 37/39 years in 2016) and 1994 (follow-up at 22/23 years in 2016) in RP, in 1982 (follow-up at 30 years in 2012) and 1993 (follow-up at 22 years ins 2015) in Pelotas, and in 1997/98 (follow-up at 18/19 years in 2016) in SL, thus representing cross-sectional data.

RP had a population of 604,682 inhabitants, a Human Development Index (HDI) of 0.80, and a Gini index (GI) of 0.546. SL had a population of 1,014,837 inhabitants in 2010, an HDI of 0.768, and a GI of 0.627. PEL had a population of 328,275 inhabitants in 2010, an HDI of 0.739, and a GI of 0.560¹⁷17. Instituto de Pesquisa Econômica Aplicada. Atlas do desenvolvimento humano no Brazil. Brasília: PNUD; 2021..

Further details about the methodology of each study have been published previously¹³13. Confortin SC, Ribeiro MRC, Barros AJD, Menezes AMB, Horta BL, Victora CG, et al. RPS Brazilian birth cohorts consortium (Ribeirão Preto, Pelotas and São Luís): history, objectives and methods. Cad Saúde Pública 2021; 37(4): e00093320. https://doi.org/10.1590/0102-311X00093320
https://doi.org/10.1590/0102-311X0009332... ,¹⁸18. Cardoso VC, Simões VMF, Barbieri MA, Silva AAM, Bettiol H, Alves MTSSB, et al. Profile of three Brazilian birth cohort studies in Ribeirão Preto, SP and São Luís, MA. Braz J Med Biol Res 2007; 40(9): 1165-76. https://doi.org/10.1590/s0100-879x2006005000148
https://doi.org/10.1590/s0100-879x200600... ,¹⁹19. Horta BL, Gigante DP, Gonçalves H, Motta JVS, Mola CL, Oliveira IO, et al. Cohort profile update: the 1982 Pelotas (Brazil) birth cohort study. Int J Epidemiol 2015; 44(2): 441, 441a, 441e. https://doi.org/10.1093/ije/dyv017
https://doi.org/10.1093/ije/dyv017... ,²⁰20. Gonçalves H, Wehrmeister FC, Assunção MCF, Tovo-Rodrigues L, Oliveira IO, Murray J, et al. Cohort profile update: the 1993 Pelotas (Brazil) birth cohort follow-up at 22 years. Int J Epidemiol 2018; 47(5): 1389-90e. https://doi.org/10.1093/ije/dyx249
https://doi.org/10.1093/ije/dyx249... and are briefly explained below.

In the RP78 cohort, we used the data from 1,775 subjects evaluated in the last follow-up in 2016/2017, at 37 to 39 years old. In the RP94 cohort, in 2016, 1,041 subjects were evaluated at 22/23 years old. In the PEL82 cohort, the data of the 2012 follow-up were used, totaling 3,701 participants evaluated at 30 years old. In the PEL93 cohort, when the participants were 22 years old, 3,810 participants were evaluated. The SL97 cohort was conducted in 2016, at 18/19 years old, and consisted of 2,515 adolescents.

DATA COLLECTION AND TOOLS

Objectively measured moderate and vigorous physical activity (MVPA)

The MVPA was measured objectively with ActiGraph^® accelerometers (GT3X and GTX3+) in SL and RP; in PEL, the GENEActiv accelerometer (ActivInsights, Kimbolton) was used for the 1982 cohort at 30 years of age, and the ActiGraph wGT3X-BT for the 1993 cohort at 22 years of age. Data on the validity of the measurements and the comparability of the different ActiGraph models have been published previously²¹21. Rowlands AV, Mirkes EM, Yates T, Clemes S, Davies M, Khunti K, et al. Accelerometer-assessed physical activity in epidemiology: are monitors equivalent? Med Sci Sports Exerc 2018; 50(2): 257-65. https://doi.org/10.1249/MSS.0000000000001435
https://doi.org/10.1249/MSS.000000000000... ,²²22. Rowlands AV, Yates T, Davies M, Khunti K, Edwardson CL. Raw accelerometer data analysis with GGIR R-package: does accelerometer brand matter? Med Sci Sports Exerc 2016; 48(10): 1935-41. https://doi.org/10.1249/MSS.0000000000000978
https://doi.org/10.1249/MSS.000000000000... ,²³23. Rowlands AV, Fraysse F, Catt M, Stiles VH, Stanley RM, Eston RG, et al. Comparability of measured acceleration from accelerometry-based activity monitors. Med Sci Sports Exerc 2015; 47(1): 201-10. https://doi.org/10.1249/MSS.0000000000000394
https://doi.org/10.1249/MSS.000000000000... . The processing of the accelerometer data involved self-calibration; in addition, the examiners were rigorously trained by the research coordinators in the placement of the device on the participants and in providing the necessary instructions.

The participants in the cohorts used the accelerometer for seven continuous days in SL97, RP78, and RP94, for 4 to 7 days in PEL82, and for 6 days in PEL93; the participants were asked to use the device on the wrist 24 hours per day (minimum usage is 2 days), including on weekends, except during showering and water-based activities.

The ActiGraph (GT3X, GTX3+, and wGT3X-BT) data were collected at a frequency of 60 Hz and the GENEActiv data at 85.7 Hz, and both brands summarized the acceleration signals over 5-second epochs for the definition of the variable of moderate-vigorous physical activity. The raw ActiGraph data were extracted with the ActiLife 6.12 software, which generated a spreadsheet (.csv) for each participant. The data were then processed for filtering non-human movements, validation of the time of use, and self-calibration using the R package (GGIR version 1.11-0). Data processing also generated data quality plots for each participant for visual inspection. The algorithm proposed by van Hess et al.²⁴24. van Hees VT, Sabia S, Anderson KN, Denton SJ, Oliver J, Catt M, et al. A novel, open access method to assess sleep duration using a wrist-worn accelerometer. PLoS One 2015; 10(11): e0142533. https://doi.org/10.1371/journal.pone.0142533
https://doi.org/10.1371/journal.pone.014... was used to identify physical activity parameters. The GENEActiv data were configured and downloaded using the GENEActiv software. The accelerometer data in binary format were analyzed using the GGIR R-package²⁵25. Knuth AG, Assunção MCF, Gonçalves H, Menezes AMB, Santos IS, Barros AJD, et al. Descrição metodológica do uso de acelerometria para mensurar a prática de atividade física nas coortes de nascimentos de Pelotas, Rio Grande do Sul, 1993 e 2004. Cad Saúde Pública 2013; 29(3): 557-65. https://doi.org/10.1590/S0102-311X2013000300013
https://doi.org/10.1590/S0102-311X201300... .

In the RP78 cohort, 1,200 subjects used the accelerometer; 31 cases were excluded because of invalid data and seven because of incomplete data (use of the device for less than two days). In the RP94 cohort, 548 subjects used the accelerometer, with the exclusion of 16 subjects because of invalid data and one because of incomplete data (use of the device for less than two days).

Over the 30 years of follow-up in the PE82 cohort, 2,876 participants used the accelerometer, with the exclusion of 152 subjects because of invalid data and 30 because they had used the device for less than two days (incomplete data). In the PE93 cohort, over the 22 years of follow-up, 3,280 participants used the accelerometer and 297 were excluded because of incomplete data (use of the device for less than two days). In the SL97 cohort, 1,538 subjects used the accelerometer; 214 cases were excluded because of invalid data and nine because of incomplete data (use of the device for less than two days).

The Spearman-Brown formula²⁶26. Eisinga R, te Grotenhuis M, Pelzer B. The reliability of a two-item scale: pearson, cronbach, or spearman-brown? Int J Public Health 2013; 58(4): 637-42. https://doi.org/10.1007/s00038-012-0416-3
https://doi.org/10.1007/s00038-012-0416-... was used in all cohorts to calculate the reliability for minimum accelerometer days. The total time of moderate and vigorous physical activity in minutes per day was used in this study. This variable was categorized into terciles, with the 3^rd tercile being defined as the most active (Supplemental Table 1).

LEISURE-TIME PHYSICAL ACTIVITY

A questionnaire that assesses the duration and weekly frequency of leisure activities was used for the analysis of self-reported leisure-time physical activity at 30 years of age in the PE82 and at 22 years of age in the PE93 cohorts. This questionnaire consists of a list of leisure activities elaborated from the results of a pilot study that identified the physical activities most frequently performed by young adults. In the RP and SL cohorts, leisure-time physical activity was evaluated using a list of physical activities obtained from the Self-Administered Physical Activity Checklist²⁷27. Farias Júnior JC, Lopes AS, Mota J, Santos MP, Ribeiro JC, Hallal PC. Validity and reproducibility of a physical activity questionnaire for adolescents: adapting the Self-Administered Physical Activity Checklist. Rev Bras Epidemiol 2012; 15(1): 198-210. https://doi.org/10.1590/s1415-790x2012000100018
https://doi.org/10.1590/s1415-790x201200... . All questions permitted the creation of a time variable in minutes of leisure-time physical activity per week. This variable was dichotomized into insufficiently active (<150 min/week) and active (³150 min/week) according to WHO recommendations³3. Bull FC, Al-Ansari SS, Biddle S, Borodulin K, Buman MP, Cardon G, et al. World Health Organization 2020 guidelines on physical activity and sedentary behaviour. Br J Sports Med. 2020; 54(24): 1451-62. https://doi.org/10.1136/bjsports-2020-102955
https://doi.org/10.1136/bjsports-2020-10... .

COVARIATES

The categorization of the sociodemographic variables was the same for all sites and cohorts.

DATA ANALYSIS

Descriptive analysis stratified by sex was used for all sociodemographic variables. The prevalence and respective 95% confidence intervals (95%CI) were calculated for self-reported leisure-time physical activity and MVPA in each city according to skin color, socioeconomic classification, and study/work activities, through Pearson’s chi-square test. The prevalence rates of the outcomes were compared between the groups, in each cohort, and between the studies (established by 95% confidence intervals) using the Kruskal-Wallis test.

The sample of each cohort was stratified by sex since logistic regression analysis revealed an interaction between sex and skin color, socioeconomic classification and study/work activities in a large number of associations with the outcomes (leisure-time and MVPA) (not presented). A 5% statistical significance level was considered. The Stata 14.0 program (Stata Corporation, College Station, USA) was used for statistical analysis.

ETHICAL ASPECTS

After they had received the necessary explanations, all participants signed the informed consent form. The studies were approved by the Ethics Committees of the local university institutions involved (Approval numbers 1.282.710 for the 1978/79 and 1994 RP birth cohorts; 16/12 and 1.250.366 for the 1982 and 1993 PEL birth cohorts, and 1.302.489 for the 1997/98 SL birth cohorts). Therefore, all projects met the criteria of the National Health Council and its complementary regulations.

RESULTS

Supplemental Table 1 shows the characteristics of the general sample and stratified by sex. Most of the participants evaluated in the follow-up were white and worked only, except for the adolescents from SL whose self-reported skin color was mostly brown and who studied only. Regarding socioeconomic classification, most adults aged 37 and 22 years from RP and those aged 30 years from PEL belonged to class A/B, while most adolescents from SL and adults aged 22 years from PEL belonged to class C.

The prevalence of active participants in terms of leisure-time physical activity in the general sample was higher among adolescents from SL and adults (22 years) from RP, and lower among adults (30 years) from PEL, with a significant difference between the cohorts. When stratified by sex, a higher prevalence of active participants was observed among men. Most male adolescents and adults were active during leisure, except for adults (30 years) from PEL, with only 38.4% reporting this activity. The highest prevalence of the outcome was observed among male adolescents living in SL. For females, a minority was active during leisure in the cohorts studied, with the highest prevalence of this outcome being among adult women (22 years) from RP.

Table 1 shows the prevalence of leisure-time physical activity and MVPA among men according to the independent variables. White (RP94) or black skin color (PEL82), belonging to socioeconomic class A/B (RP78, PEL82, PEL93), studying and working (PEL93), and not studying (SL97) were associated with a higher prevalence of leisure-time physical activity in these cohorts.

Concerning MVPA, black (RP78, PEL93, SL97) or brown skin color (RP94, PEL82), belonging to socioeconomic classes C (RP78) and D/E (PEL82, PEL93, SL97), not studying (PEL82), and working only (PEL93) were associated with a higher prevalence of this outcome (Table 1).

The prevalence of leisure-time physical activity and MVPA among women in each cohort according to the independent variables is shown in Table 2. White skin color (RP94), belonging to class A/B (RP78, RP94, PEL82, PEL93), studying and working (RP78, PEL82), and studying only (PEL93) were associated with a higher prevalence of leisure-time physical activity. The prevalence of leisure-time physical activity was higher among women aged 37/39 years (RP78) who study and work; white and black women aged 22 years (RP94) who belong to class A/B and who do not study/work or work only, and brown women aged 18 years (SL97) who belong to class C and D/E and who work only when compared to the other cohorts (Table 2).

Regarding MVPA, black (43.0% in PE93) or brown skin color (47.1% in PE82), belonging to class D/E (54.5% in RP78, 58.4% in PE82, 43.3% in PE93, and 40.6% in SL97), not working (41.0% in PE82), and working only (37.7% in PE93) were associated with a higher prevalence of this outcome (Table 2).

DISCUSSION

In all cohorts, the prevalence of leisure-time and MVPA was higher among men and leisure-time physical activity was more prevalent in SL and RP, while the level of MVPA (3^rd tercile) was higher in PEL and SL. In general, the younger cohorts of RP and PEL were more active during leisure time but the same was not observed for MVPA. The results of this study also showed that the variation in physical activity was associated with gender and sociodemographic conditions according to the study site.

Variation in physical activity practice according to the region, environment, and sociodemographic and cultural characteristics has been reported in the literature³⁰30. Brasil. Ministério da Saúde. Vigitel Brasil 2018: vigilância de fatores de risco e proteção para doenças crônicas por inquerito telefônico. Brasília: Ministério da Saúde; 2018. [acessado em 29 jul. 2022]. Disponível em: https://www.gov.br/saude/pt-br/centrais-de-conteudo/publicacoes/publicacoes-svs/vigitel/vigitel-brasil-2018.pdf/@@download/file/vigitel-brasil-2018.pdf
https://www.gov.br/saude/pt-br/centrais-... ,³¹31. Mielke GI, Malta DC, Sá GBAR, Reis RS, Hallal PC. Diferenças regionais e fatores associados à prática de atividade física no lazer no Brasil: resultados da Pesquisa Nacional de Saúde-2013. Rev Bras Epidemiol 2015; 18 Suppl 2: 158-69. https://doi.org/10.1590/1980-5497201500060014
https://doi.org/10.1590/1980-54972015000... . In the present study, the city of SL had a higher prevalence of physical activity practitioners during leisure time. Some characteristics of the SL97 cohort³²32. Jonck VTF, Araujo CCR, Hammes JF, Pazin J, Boing L, Rocha M, et al. Atividade física associada ao ambiente urbano: um estudo com mulheres de três cidades litorâneas de Santa Catarina. Rev Bras Educ Fís Esporte 2018; 32(2): 253-61. https://doi.org/10.11606/1807-509201800020253
https://doi.org/10.11606/1807-5092018000... , for example, being the only one located in a coastal city³²32. Jonck VTF, Araujo CCR, Hammes JF, Pazin J, Boing L, Rocha M, et al. Atividade física associada ao ambiente urbano: um estudo com mulheres de três cidades litorâneas de Santa Catarina. Rev Bras Educ Fís Esporte 2018; 32(2): 253-61. https://doi.org/10.11606/1807-509201800020253
https://doi.org/10.11606/1807-5092018000... , being the youngest³¹31. Mielke GI, Malta DC, Sá GBAR, Reis RS, Hallal PC. Diferenças regionais e fatores associados à prática de atividade física no lazer no Brasil: resultados da Pesquisa Nacional de Saúde-2013. Rev Bras Epidemiol 2015; 18 Suppl 2: 158-69. https://doi.org/10.1590/1980-5497201500060014
https://doi.org/10.1590/1980-54972015000... , and having a higher frequency of participants who study only, contribute to the results observed. Despite this, SL97 showed a greater difference between sexes in leisure-time physical activity, corroborating data that indicate less practice among women in the north and northeast of Brazil compared to men in these regions³¹31. Mielke GI, Malta DC, Sá GBAR, Reis RS, Hallal PC. Diferenças regionais e fatores associados à prática de atividade física no lazer no Brasil: resultados da Pesquisa Nacional de Saúde-2013. Rev Bras Epidemiol 2015; 18 Suppl 2: 158-69. https://doi.org/10.1590/1980-5497201500060014
https://doi.org/10.1590/1980-54972015000... .

Hallal et al.⁷7. Hallal PC, Victora CG, Wells JCK, Lima RC. Physical inactivity: prevalence and associated variables in Brazilian adults. Med Sci Sports Exerc 2003; 35(11): 1894-900. https://doi.org/10.1249/01.MSS.0000093615.33774.0E
https://doi.org/10.1249/01.MSS.000009361... observed no disparity between genders when different physical activity domains were analyzed with a questionnaire. In contrast, women were less active in the present study, even in MVPA. Although the present results do not allow to identify in which physical activity domains these differences occurred, the lower engagement of women in leisure-time physical activity may explain the low prevalence of MVPA in this group.

Concerning leisure-time physical activity, some studies have reported differences between genders³⁰30. Brasil. Ministério da Saúde. Vigitel Brasil 2018: vigilância de fatores de risco e proteção para doenças crônicas por inquerito telefônico. Brasília: Ministério da Saúde; 2018. [acessado em 29 jul. 2022]. Disponível em: https://www.gov.br/saude/pt-br/centrais-de-conteudo/publicacoes/publicacoes-svs/vigitel/vigitel-brasil-2018.pdf/@@download/file/vigitel-brasil-2018.pdf
https://www.gov.br/saude/pt-br/centrais-... ,³¹31. Mielke GI, Malta DC, Sá GBAR, Reis RS, Hallal PC. Diferenças regionais e fatores associados à prática de atividade física no lazer no Brasil: resultados da Pesquisa Nacional de Saúde-2013. Rev Bras Epidemiol 2015; 18 Suppl 2: 158-69. https://doi.org/10.1590/1980-5497201500060014
https://doi.org/10.1590/1980-54972015000... ,³³33. Las Casas RCR, Bernal RTI, Jorge AO, Melo EM, Malta DC. Fatores associados à prática de atividade física na população brasileira - Vigitel 2013. Saúde Debate 2018; 42(spe4): 134-44. https://doi.org/10.1590/0103-11042018S410
https://doi.org/10.1590/0103-11042018S41... . A possible explanation could be the fact that, since school age, boys are more encouraged by family members, colleagues, and institutions to participate in physical activity as a form of leisure and social interaction³⁴34. Sallis JF, Prochaska JJ, Taylor WC. A review of correlates of physical activity of children and adolescents. Med Sci Sports Exerc 2000; 32(5): 963-75. https://doi.org/10.1097/00005768-200005000-00014
https://doi.org/10.1097/00005768-2000050... ,³⁵35. Clark S, Paechter C. ‘Why can’t girls play football?’ Gender dynamics and the playground. Sport, Education and Society. 2007; 12(3): 261-76. https://doi.org/10.1080/13573320701464085
https://doi.org/10.1080/1357332070146408... . Within this context, measures encouraging regular physical activity that provide equal opportunities for women as early as childhood are fundamental to reducing inequalities in leisure-time physical activity³⁶36. Brown WJ, Mielke GI, Kolbe-Alexander TL. Gender equality in sport for improved public health. Lancet 2016; 388(10051): 1257-8. https://doi.org/10.1016/S0140-6736(16)30881-9
https://doi.org/10.1016/S0140-6736(16)30... , considering that individuals who are more active in childhood and adolescence tend to exercise more frequently in adulthood³⁷37. Kraut A, Melamed S, Gofer D, Froom P, CORDIS Study. Effect of school age sports on leisure time physical activity in adults: the CORDIS study. Med Sci Sports Exerc 2003; 35(12): 2038-42. https://doi.org/10.1249/01.MSS.0000099087.96549.96
https://doi.org/10.1249/01.MSS.000009908... .

The socio-economic classification showed that, for both genders, participants of higher socioeconomic status were more physically active during leisure time, while those of lower status exhibited lower total physical activity levels. Lack of money and time and tiredness have been reported as perceived barriers that discourage people from engaging in leisure-time physical activity³⁸38. Reichert FF, Barros AJD, Domingues MR, Hallal PC. The role of perceived personal barriers to engagement in leisure-time physical activity. Am J Public Health 2007; 97(3): 515-9. https://doi.org/10.2105/AJPH.2005.070144
https://doi.org/10.2105/AJPH.2005.070144... . A large proportion of the Brazilian population still associates physical activity exclusively with physical exercise and/or sports performed in clubs or fitness centers, which often require a financial investment³⁸38. Reichert FF, Barros AJD, Domingues MR, Hallal PC. The role of perceived personal barriers to engagement in leisure-time physical activity. Am J Public Health 2007; 97(3): 515-9. https://doi.org/10.2105/AJPH.2005.070144
https://doi.org/10.2105/AJPH.2005.070144... . On the other hand, data from the National Health Survey indicate that the most common leisure-time physical activity among Brazilians is walking, possibly because it is easy to incorporate into daily life, in addition to its low cost and easy access³⁹39. Wendt A, Carvalho WRG, Silva ICM, Mielke GI. Preferências de atividade física em adultos brasileiros: resultados da Pesquisa Nacional de Saúde. Rev Bras Ativ Fís Saúde 2019; 24: 1-9. https://doi.org/10.12820/rbafs.24e0079
https://doi.org/10.12820/rbafs.24e0079... . However, even in the case of activities in which financial issues supposedly would not be a barrier, the lack of infrastructure, as well as a high crime rate and perceived insecurity, which are more common in underprivileged regions, are barriers that discourage people from leisure-time physical activity⁴⁰40. Florindo AA, Salvador EP, Reis RS, Guimarães VV. Perception of the environment and practice of physical activity by adults in a low socioeconomic area. Rev Saude Publica 2011; 45(2): 302-10. https://doi.org/10.1590/s0034-89102011000200009
https://doi.org/10.1590/s0034-8910201100... . Nevertheless, in agreement with other studies, the present results showed that class D/E was the most active in total physical activity, probably because of the large number of activities performed during work, commuting, and domestic tasks⁴¹41. Azevedo MR, Araújo CLP, Reichert FF, Siqueira FV, Silva MC, Hallal PC. Gender differences in leisure-time physical activity. Int J Public Health 2007; 52(1): 8-15. https://doi.org/10.1007/s00038-006-5062-1
https://doi.org/10.1007/s00038-006-5062-... .

Study⁴²42. Herazo-Beltrán Y, Pinillos Y, Vidarte J, Crissien E, Suarez D, García R. Predictors of perceived barriers to physical activity in the general adult population: a cross-sectional study. Braz J Phys Ther 2017; 21(1): 44-50. https://doi.org/10.1016/j.bjpt.2016.04.003
https://doi.org/10.1016/j.bjpt.2016.04.0... also demonstrated that economic class is an important factor in motivating the practice of leisure-time physical activity. On the other hand, the economic aspect is not a barrier frequently reported in developed countries⁴³43. Booth ML, Bauman A, Owen N, Gore CJ. Physical activity preferences, preferred sources of assistance, and perceived barriers to increased activity among physically inactive Australians. Prev Med 1997; 26(1): 131-7. https://doi.org/10.1006/pmed.1996.9982
https://doi.org/10.1006/pmed.1996.9982... ,⁴⁴44. Sørensen M, Gill DL. Perceived barriers to physical activity across Norwegian adult age groups, gender and stages of change. Scand J Med Sci Sport 2008; 18(5): 651-63. https://doi.org/10.1111/j.1600-0838.2007.00686.x
https://doi.org/10.1111/j.1600-0838.2007... . Thus, the results of the present study, together with the evidence in the literature, reinforce the importance of encouraging the practice of leisure-time physical activity, especially for the most socially vulnerable groups in developing countries.

Black and brown participants exhibited higher MVPA levels but the same was not observed for leisure-time physical activity. A possible explanation could be the socioeconomic and demographic disparities observed in Brazil among blacks, browns, and whites. A lower educational level and socioeconomic status are generally associated with higher levels of work-related physical activity⁴⁵45. Marquez DX, Neighbors CJ, Bustamante EE. Leisure time and occupational physical activity among racial or ethnic minorities. Med Sci Sports Exerc 2010; 42(6): 1086-93. https://doi.org/10.1249/MSS.0b013e3181c5ec05
https://doi.org/10.1249/MSS.0b013e3181c5... . Furthermore, a low educational level and socioeconomic status are more frequently observed among browns and blacks when compared to whites⁴⁶46. Chor D, Lima CRA. Aspectos epidemiológicos das desigualdades raciais em saúde no Brasil. Cad Saúde Pública 2005; 21(5): 1586-94. https://doi.org/10.1590/s0102-311x2005000500033
https://doi.org/10.1590/s0102-311x200500... . Although subjects belonging to class D/E and with black/brown skin color performed more MVPA, findings regarding the gains related to this activity are still inconsistent. Some studies have reported an increased risk of cardiovascular diseases and mortality associated with high occupational physical activity⁴⁷47. Holtermann A, Hansen JV, Burr H, Søgaard K, Sjøgaard G. The health paradox of occupational and leisure-time physical activity. Br J Sports Med 2012; 46(4): 291-5. https://doi.org/10.1136/bjsm.2010.079582
https://doi.org/10.1136/bjsm.2010.079582... ,⁴⁸48. Holtermann A, Krause N, van der Beek AJ, Straker L. The physical activity paradox: six reasons why occupational physical activity (OPA) does not confer the cardiovascular health benefits that leisure time physical activity does. Br J Sports Med 2018; 52(3): 149-50. https://doi.org/10.1136/bjsports-2017-097965
https://doi.org/10.1136/bjsports-2017-09... ,⁴⁹49. Li J, Loerbroks A, Angerer P. Physical activity and risk of cardiovascular disease: what does the new epidemiological evidence show? Curr Opin Cardiol 2013; 28(5): 575-83. https://doi.org/10.1097/HCO.0b013e328364289c
https://doi.org/10.1097/HCO.0b013e328364... .

Regarding work and/or study, the present results indicate a lower frequency of leisure-time physical activity among women of the RP78, RP94, and PE93 cohorts who neither study nor work. However, this association was not observed for total physical activity. This finding can be explained in part by the fact that a proportion of women who are neither engaged in paid work nor study perform domestic tasks only. For example, in PE82, only 13.3% of the women who neither study nor work were active during leisure time, while 41% of these women remained in the most active tercile of MVPA. In men, the prevalence of leisure-time physical activity was higher among those who neither work nor study in SL97 and among those who study and work in PE93. However, the prevalence of MVPA was higher among those who neither study nor work in PE82 and among those who work only in PE93. Men who study only or study and work are more frequently inactive, for example, spending more time sitting. Men who neither work nor study have more time for leisure-time activities and can spend more time on domestic tasks, increasing total physical activity, whereas those who study and work may try to compensate for the sedentary time by having more leisure-time physical activity.

We chose self-reporting for the measurement of leisure-time physical activity, which may have resulted in information bias due to the difficulty of accurately estimating physical activity. However, errors in the estimates are greater for the domains of work-related and domestic physical activity⁵⁰50. Sebastião E, Gobbi S, Chodzko-Zajko W, Schwingel A, Papini CB, Nakamura PM, et al. The International Physical Activity Questionnaire-long form overestimates self-reported physical activity of Brazilian adults. Public Health 2012; 126(11): 967-75. https://doi.org/10.1016/j.puhe.2012.07.004
https://doi.org/10.1016/j.puhe.2012.07.0... ; in the present study, these domains were only assessed when the global accelerometer-measured physical activity was used, which provides a measure of total physical activity without distinguishing between domains. In addition, questionnaires allowed to measure leisure-time physical activity in studies involving a large number of participants, and specific domains can only be assessed by self-report. Thus, the use of different questionnaires can be considered a limitation, although categorization was aimed at attenuating the differences between the measures. Another limitation is that the use of self-reported measures for skin color and socioeconomic classification can result in information bias; however, this information was obtained using validated instruments. In addition, differences in the age of the participants can impair the comparison between the cohorts. Due to the cross-sectional design, it is not possible to establish a causal relationship between sociodemographic indicators and outcomes.

The strengths of the study, the sample size of the cohorts and the use of an objective measure in large samples, are uncommon in Brazilian studies. We also standardized the analysis with accelerometers. In addition, to our knowledge, this is the first study analyzing the physical activity data of cohorts followed up in different regions of Brazil using a similar methodology.

The results demonstrated a difference in leisure-time and total physical activity between the regions. The factors associated with physical activity were similar between the cohorts despite demographic and economic differences. Regardless of the location, the data suggest that sociodemographic characteristics should be considered when promoting leisure-time physical activity and actions should be aimed at young people and adults of both genders who are socioeconomically more vulnerable. These factors must be taken into account when creating public policies designed to encourage leisure-time physical activity to improve the health conditions of the population. On the other hand, total physical activity must be analyzed with caution since it represents overall physical activity and is not separated by domains.

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