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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., (© 2022. The Author(s).)

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., Competing Interests: Competing interests: The authors completed the ICMJE Unified Competing Interest Form (available upon request from the corresponding author), and declare no conflicts of interest., (Copyright © 2022 by the Journal of Global Health. All rights reserved.)

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.

Quarantine periods change routines and behaviors with potential impact on different health outcomes. To determine the association between changes in physical activity and sedentary behaviors with changes in back pain during the COVID-19 pandemic quarantine among Brazilian adults. This was a nationwide survey through online questionnaires using data from 43,062 adults (≥ 18 years of age). Information on back pain was assessed using questions about episodes of back pain and worsening symptoms during the quarantine. The pattern of movement behaviors adopted before and during the pandemic were considered for physical inactivity (< 150 min/week of activity), high TV-viewing (≥ 4 h/d), and high computer/tablet use (≥ 4 h/d). Covariates included sex, age group, academic achievement, skin color, working status during the quarantine, and adherence to the quarantine. Logistic regression models were used for statistical analyses (weighted for national representativity). Becoming inactive (OR=1.76, 95% CI: 1.32, 2.37), with high TV-viewing (OR=1.35, 95% CI: 1.14, 1.61) and high computer/tablet use (OR=1.39, 95% CI: 1.11, 1.73) during the pandemic were associated with a higher incidence of back pain. The incidence of physical inactivity was also associated with increased back pain (OR=2.71, 95%CI: 1.64, 4.48). We conclude that increased physical inactivity and sedentary behaviors due to the COVID-19 pandemic quarantine are associated with the incidence and worsening symptoms of back pain among Brazilian adults. [ABSTRACT FROM AUTHOR]

Purpose: Our aim was to verify the incidence of physical inactivity and excessive screen time during the first wave of the COVID-19 pandemic among Brazilian adults, as well as to identify subgroups that are more affected by the quarantine actions.Methods: The data of 39,693 Brazilian adults were collected through an online questionnaire between April 24th and May 24th, 2020. Information about physical activity (weekly frequency and daily duration), TV viewing, and computer/tablet use (daily duration) before and during the pandemic period were reported. The correlates adopted were sex, age group, highest academic achievement, skin color, per capita income, country macro region, working status during the quarantine, and adherence to the quarantine. Logistic regression models were used.Results: The incidence of physical inactivity (<150 min/week), high TV viewing (≥4 h/d), and computer/tablet use (≥4 h/d), were, respectively, 70.4%, 31.4%, and 37.9% during the COVID-19 pandemic. The younger age group showed higher incidences of physical inactivity (78%) and high computer/tablet use (59%), while middle-age adults (30-59 years) showed a higher incidence of TV viewing (34%). People who adhered to stricter measures of quarantine presented a higher incidence of excessive screen time.Conclusion: High incidences of physical inactivity and excessive screen time were identified in specific population subgroups during the first wave of the COVID-19 pandemic in Brazil. [ABSTRACT FROM AUTHOR]

Background: Our aim was to analyze the associations between multiple lifestyle behaviors and depressive symptoms.Methods: We included 4,725 adults (18-59y), that provided data in routine health evaluations of a hospital in Brazil, followed for a mean period of 3.1 ± 1.6 years. Physical activity, alcohol consumption (measured using Alcohol Use Disorders Identification Test) and tobacco smoking were categorized as: (1) absence of the behavior (inactivity i.e. not complying with 150 min of moderate-to-vigorous PA/week, not smoking, no risky drinking, i.e. AUDIT<5) during baseline and follow-up; (2) Absence during baseline and presence during follow-up; (3) Presence during baseline and absence during follow-up; (4) Presence during both time points. Depressive symptoms were measured with the Beck Inventory was adopted to analyze patterns of depressive symptoms over time (as exposure). C-reactive protein [HS-CRP]) was assessed and its role in the association was tested. Incidence indicators of behaviors and depressive symptoms were created and used as outcomes. We used crude and adjusted Poisson regression analysis.Results: Fully adjusted models revealed that persistently physical inactive participants (RR:1.71;95%CI:1.33-2.21), those who became physically inactive (1.68;1.19-2.26), with consistently risky drinking (1.62;1.15-2.30), and who became risky drinkers (1.62;1.15-2.30) had higher risk for incidence of elevated depressive symptoms. Vice versa participants with incidence of depressive symptoms over time presented higher risk for physical inactivity (1.44;1.11-1.87) and risky drinking (1.65;1.16-2.34) incidence. HS-CRP did not influence the associations.Limitations: Self-reported physical activity, binary tobacco smoking, and non-probabilistic sampling.Conclusions: There is a prospective relationship between elevated depressive symptoms and adverse lifestyle behaviors. [ABSTRACT FROM AUTHOR]