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Leandro Martin Totaro Garcia, Kelly Samara da Silva, Giovâni F. Del Duca, Filipe Ferreira da Costa, and Markus Vinicius Nahas

Background:

Our purpose was to examine the association of television viewing (hours/day), sedentary work (predominantly sitting at work), passive transportation to work (car or motorcycle), and the clustering of these behaviors (“sedentary lifestyle”), as well as leisure-time physical inactivity (LTPI), with chronic diseases (hypertension, hypercholesterolemia, type 2 diabetes, obesity, and clustering of chronic diseases) in Brazilian workers.

Methods:

Cross-sectional study conducted from 2006 to 2008 in 24 Brazilian federal units (n = 47,477). A questionnaire was applied. Descriptive statistics, binary and multinomial logistic regressions were used.

Results:

Magnitude of association with chronic diseases varied greatly across domains and gender. Sedentariness at work was the most consistent behavior associated with chronic diseases, especially in men (ORhypertension = 1.10, 95% CI: 1.01–1.20; ORhypercholesterolemia = 1.34, 95% CI: 1.21–1.48; ORobesity = 1.27, 95% CI: 1.15–1.41; OR1chronic disease = 1.17, 95% CI: 1.09–1.26; OR≥2chronic diseases = 1.61, 95% CI: 1.46–1.78) compared with women (ORhypercholesterolemia = 1.15, 95% CI: 1.01–1.31; ORobesity = 1.24, 95% CI: 1.04–1.48). LTPI was associated with all diseases in men (except type 2 diabetes), but only with obesity in women.

Conclusion:

Adverse health consequences may be differently associated according to behavior domain and gender. Sedentary work and LTPI were consistently associated with chronic disease in Brazilian workers, especially in men.

Open access

Deborah Salvo, Leandro Garcia, Rodrigo S. Reis, Ivana Stankov, Rahul Goel, Jasper Schipperijn, Pedro C. Hallal, Ding Ding, and Michael Pratt

Background: Many of the known solutions to the physical inactivity pandemic operate across sectors relevant to the United Nations Sustainable Development Goals (SDGs). Methods: The authors examined the contribution of physical activity promotion strategies toward achieving the SDGs through a conceptual linkage exercise, a scoping review, and an agent-based model. Results: Possible benefits of physical activity promotion were identified for 15 of the 17 SDGs, with more robust evidence supporting benefits for SDGs 3 (good health and well-being), 9 (industry, innovation, and infrastructure), 11 (sustainable cities and communities), 13 (climate action), and 16 (peace, justice, and strong institutions). Current evidence supports prioritizing at-scale physical activity-promoting transport and urban design strategies and community-based programs. Expected physical activity gains are greater for low-and middle-income countries. In high-income countries with high car dependency, physical activity promotion strategies may help reduce air pollution and traffic-related deaths, but shifts toward more active forms of travel and recreation, and climate change mitigation, may require complementary policies that disincentivize driving. Conclusions: The authors call for a synergistic approach to physical activity promotion and SDG achievement, involving multiple sectors beyond health around their goals and values, using physical activity promotion as a lever for a healthier planet.

Restricted access

Giovâni F. Del Duca, Leandro Martin Totaro Garcia, Shana Ginar da Silva, Kelly Samara da Silva, Elusa S. Oliveira, Mauro V. Barros, and Markus V. Nahas

Background:

Physical inactivity in each domain (leisure, work, commuting, and household) is not completely independent. This study aimed to describe the clustering of physical inactivity in different domains and its association with sociodemographic factors among Brazilian industrial workers.

Methods:

This was a cross-sectional, population-based study using data from 23 Brazilian states and the Federal District collected via questionnaires between 2006 and 2008. Physical inactivity in each domain was defined as nonparticipation in specific physical activities. Clustering of physical inactivity was identified using the ratio of the observed (O) and expected (E) percentages of each combination. Multinomial logistic regression was used to identify sociodemographic factors with the outcome.

Results:

Among the 44,477 interviewees, most combinations exceeded expectations, particularly the clustering of physical inactivity in all domains among men (O/E = 1.37; 95% CI: 1.30; 1.44) and women (O/E = 1.47; 95% CI: 1.36; 1.60). Physical inactivity in 2 or more domains was observed more frequently in women, older age groups, individuals living without a partner, and those with higher education and income levels.

Conclusions:

Physical inactivity tends to be observed in clusters regardless of gender. Women and workers with higher income levels were the main factors associated with to be physically inactive in 2 or more domains.