Background: The authors analyzed time trends and sociodemographic inequalities in different physical activity and sedentary behavior domains between 2003 and 2019. Methods: A secondary analysis of data from 5 cross-sectional Brazilian epidemiological surveys (World Health Survey—2003, National Household Sample Survey—2008/2015, and Brazilian Health Survey—2013/2019) conducted among a nationally representative sample of Brazilian adults. The authors used data on different domains of physical activity (leisure, commute, total transport, and total physical activity) and sedentary behavior (TV viewing and other types of screens) that were available in the different surveys. Gender, age group, country region, ethnicity, type of area and city, and quintiles of income and educational achievement were used as sociodemographic correlates. Results: The prevalence of leisure-time physical activity increased over time (2008: 7.0% vs 2019: 26.5%). There was also an increased trend of social inequality in leisure-time physical activity. A trend of reduction was observed for active commuting (2008: 35.0% vs 2019: 21.8%), while total transport physical activity was stable (2013: 49.5% vs 2019: 49.6%). Directions of findings were opposite for sedentary behavior, with reduced trend for >3 hours per day of TV viewing (2008: 34.8% vs 2019: 21.8%) and increased trend for >3 hours per day of other types of screen time (2008: 6.4% vs 2019: 22.2%). Conclusion: A positive trend exists in leisure-time physical activity, but there was also an increase in social inequalities for physical activity in Brazil.

Background To identify the prevalence and sociodemographic correlates of different domains of physical activity (PA) and higher sitting time among South American adolescents. Methods Data from national surveys of 11 South American countries were analyzed, and comprised information on 166,901 adolescents. PA (≥ 60 min/day of moderate-vigorous PA), physical education classes (PEC) (≥ 3 classes/wk), active commuting to school (≥ 1 d/wk), and higher sitting time (≥ 3 h/d) were self-reported. Sociodemographic correlates, such as gender, age, and food security status were explored using a random effect meta-analysis for logistic parameters. Results Recommended PA ranged between 7.5% (Brazil) and 19.0% (Suriname). Peru (2.2%) and Guyana (43.1%) presented the lowest prevalence of PEC and active commuting to school, respectively. Higher sitting time was less prevalent in Bolivia (24.6%) and more prevalent in Argentina (55.6%). Compared to girls, boys were more prone to reach recommendations for PA [OR = 1.94(1.65;2.28)]; to reach ≥ 3 PEC [OR = 1.17(1.04;1.33)] and to be active in commuting to school [(OR = 1.14(1.06;1.23)], but less prone to higher sitting time [OR = 0.89(0.82;0.96)]. Older adolescents had less odds of reach PA guidelines [OR = 0.86(0.77; 0.97)] and accumulated higher sitting time [OR = 1.27(1.14;1.41)]. Adolescents with food insecurity reported more PEC [OR = 1.12(1.04;1.21)] and active commuting to school [OR = 1.12(1.02;1.22)] but had less higher sitting time than their food security pairs [OR = 0.89(0.81;0.98)]. Conclusions Few adolescents reach the PA recommendation. Actions aiming the promotion of PA and the reduction of sitting time must consider girls and older adolescents as target groups, as well as the specifics of each country.

Background We aimed to investigate time trends and inequalities of different physical activity (PA) domains and sitting time (ST) in adults from South American countries. Methods We included cross-sectional data of nationally representative surveys on adults (n = 597 843) from nine South American countries (Argentina, Bolivia, Brazil, Chile, Colombia, Ecuador, Peru, Uruguay, and Venezuela), with data collection time frames ranging from 2005 to 2020. Data on different PA domains (leisure-time, transport, and occupational) and ST were assessed through questionnaires. Trends according to education level (quintiles), gender (m/w), and age group (18-34 years, 35-49 years, 50-64 years) were estimated for the harmonized indicators of nonzero PA in the different domains, ≥150 min/week of total PA and ≥8 hours/d of ST. Results Chile (2009/2010 = 78.9% vs 2016/2017 = 70.5%), and Peru (2009/2010 = 78.6% vs 2011 = 69.6%) reduced total PA, while Brazil (2013 = 57.3% vs 2019 = 67.0%) and Uruguay (2006 = 69.4% vs 2013 = 79.4%) increased, and Argentina and Venezuela maintained. There was an increasing trend for ST in Argentina, Peru, and Uruguay. Leisure-time PA increased in most countries (6/8 countries). Transport PA was relatively stable, while occupational PA presented mixed findings. Education inequalities increased over time for total and leisure-time PA, while age and gender inequalities were relatively constant. Conclusions Future South American countries' efforts may be warranted to promote PA and reduce ST in adults, while addressing inequalities when implementing actions.

We aimed to investigate correlates of TV viewing and other types of screen-based behaviors in a nationally representative sample of Brazilian adults. In the 2019 Brazilian National Health Survey (including 88,509 adults), TV viewing time and other types of screen behaviors (computer, tablet, and cellphone use) were self-reported and different geographical, sociodemographic, behavioral, and health status factors were investigated as potential correlates. Multinomial logistic regression models were used for the main analyses. Living in capital cities, urban areas, being unemployed, high consumption of soft drinks, obesity, and elevated depressive symptoms were each associated with more TV viewing and more time using other types of screens. There were differential associations between TV viewing and the use of other types of screen across age and socioeconomic variables. For instance, younger adults have a more diverse portfolio of screen time than older adults. To conclude, levels of screen-based behaviors vary by geographical, sociodemographic, behavioral, and health status characteristics. Interventions should focus on high-risk population groups and may benefit from targeting specific sedentary behaviors of interest.

Background Our aim was to analyze the association of the presence of public physical activity (PA) facilities and participation in public PA programs with leisure-time PA, with an emphasis on the moderating role of educational level and income. Methods We used data of 88,531 adults (46,869 women), with a mean age of 47.2 ± 17.1y, from the 2019 Brazilian National Health Survey. Leisure-time PA (dichotomized considering 150 min/week), the presence of a public PA facility near the household (yes or no), participation in public PA programs (yes or no), educational level (divided into quintiles) and per capita income (divided into quintiles) were all self-reported through interviews. Adjusted logistic regression models were used for the analyses. Results The presence of public PA facilities near the household and the participation in public PA programs were associated with higher leisure-time PA among all quintiles of income and educational level. However, multiplicative interactions revealed that participating in PA programs [Quintile (Q)1: OR: 13.99; 95%CI: 6.89–28.38 vs. Q5: OR: 3.48; 95%CI: 2.41–5.01] and the presence of public PA facilities near the household (Q1: OR: 3.07; 95%CI: 2.35–4.01 vs. Q5: OR: 1.38; 95%CI: 1.22–1.55) were more associated with higher odds of being active in the leisure-time among the lowest quintile of educational level. Conclusions The presence of public PA facilities and participation in public PA programs are environmental correlates that may be relevant for designing effective public health interventions to reduce social inequalities in leisure-time PA among adults in low-income areas.

O presente estudo objetiva analisar o impacto das doenças cardiovasculares (DCV) na expectativa de vida (EV) da população brasileira e identificar como a EV seria afetada pela redução hipotética de 5% a 30% e pela eliminação da mortalidade por essas doenças. Trata-se de um estudo de simulação que utiliza dados transversais a nível nacional. A análise foi realizada por meio do modelo de tábuas de vida de múltiplo decremento, considerando reduções na mortalidade por DCV de 5% a 30%, assim como a sua eliminação. Os ganhos potenciais estimados na EV, em anos e porcentagem, por meio da eliminação das doenças isquêmica, hipertensiva e cerebrovascular foram de, respectivamente, para homens e mulheres, 1,44 (2%) e 1,31 (1,7%), 0,51 (0,7%) e 0,75 (1%), 1,28 (1,8%) e 1,62 (2,1%). Os maiores ganhos ocorrem entre os residentes na região Nordeste. Além disso, para a população em geral, os ganhos estimados em EV a partir de uma redução de 5% na mortalidade por DCV, em homens e mulheres, foram de 0,07 (0,1%) e 0,06 (0,08%), para doença isquêmica, 0,02 (0,03%) e 0,04 (0,05%), para doença hipertensiva, e 0,06 (0,08%) e 0,07 (0,09%), para doença cerebrovascular. Já uma redução hipotética de 30% na mortalidade por DCV foi acompanhada de ganhos na EV, para homens e mulheres, de 0,41 (0,6%) e 0,37 (0,5%), para doença isquêmica, 0,15 (0,2%) e 0,22 (0,3%), para doença hipertensiva, e 0,36 (0,5%) e 0,45 (0,6%), para doença cerebrovascular. Assim, mais investimentos e esforços orientados para a prevenção de DCV aumentariam a EV no Brasil, principalmente nas regiões menos desenvolvidas.

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We analyzed the associations of screen-based behaviors with obesity, hypertension, and diabetes, and the moderation of different physical activity (PA) domains in these associations. We used data from the 2019 Brazilian National Health Survey, including data from 80,940 adults (mean age of 32.6 years). TV viewing, other screens (PC, tablet, and cell phone), PA domains (leisure-time, occupational, and transport) were collected via interview. Logistic regression models were used. There was a dose–response association of higher TV viewing with diabetes. Within the groups with medium and higher time spent on other screens, those with < 150 min/week in leisure-time PA increased the odds for obesity [1–2.9 h/day: OR = 1.18 (1.01, 1.39)] and hypertension [1–2.9 h/day: OR = 1.29 (1.08, 1.53); ≥ 6 h/day: OR = 1.47 (1.03, 2.09)]. Likewise, among the participants who spent ≥ 6 h/day of TV viewing, those with < 150 min/week of occupational PA presented higher odds for hypertension [OR = 1.61 (1.03, 2.53)]. In the group with higher use of other screens, < 150 min per week of occupational PA was associated with lower odds for obesity [1–2.9 h/day: OR = 0.81 (0.68, 0.97)] and hypertension [≥ 6 h/day: OR = 0.65 (0.44, 0.98)]. In conclusion, the associations of other screens with obesity and hypertension were strongest among those without leisure-time PA, while the moderator role of occupational PA was not clear.