Journal of Physical Activity and Health, 2014, 11, 1622  -1634 http://dx.doi.org/10.1123/jpah.2012-0423 © 2014 Human Kinetics, Inc.

Official Journal of ISPAH www.JPAH-Journal.com ORIGINAL RESEARCH

Sedentary Behaviors, Leisure-Time Physical Inactivity, and Chronic Diseases in Brazilian Workers: A Cross Sectional Study 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. Keywords: sedentary lifestyle, physical activity, adult, occupational health, risk factors Currently, it is well established that being physically inactive (ie, not practicing moderate-to-vigorous physical activity) is different from spending too much time in sedentary activities (ie, engaging in sitting or lying activities with a metabolic equivalent ranging from 1.0 to 1.5).1,2 Although they are different behaviors, they could synergistically increase risk of diseases. The available evidence strongly suggests that physical inactivity has an important deleterious influence on health, contributing to the development of chronic diseases such as obesity, hypertension, hypercholesterolemia, and type 2 diabetes.3,4 Moreover, knowledge about the health implications of sedentary behavior is growing rapidly.1,2 However, there are still uncertainties regarding the various chronic diseases that may be positively associated with sedentary behavior. Because of the contradictory results found in previous studies, further research on this topic is required.5,6 Like physical activity, sedentary behavior may be present in different domains of life. In high- and middle-income countries, leisure (such as watching television), work, and transportation are 3 important domains in which sedentary behavior occurs in adults.7 However, most studies have investigated the association of chronic diseases with sedentary behavior by analyzing only one of these domains or by using data from accelerometers, which cannot distinguish among the domains. Thus, little is known about whether specific domains have a stronger or more consistent positive association with chronic diseases independent of the others. Based on this information, interventions planning could take into account Garcia ([email protected]) is with the School of Public Health, University of São Paulo, São Paulo, SP, Brazil. Del Duca, da Silva, da Costa, and Nahas are with the Physical Education Dept, Federal University of Santa Catarina, Florianópolis, SC, Brazil. 1622

the most appropriate settings or domains for achieving the greatest positive impact on health. Therefore, this study examined the association of television viewing, sedentary work (predominantly seated at work), passive transportation to work (car or motorcycle), and the clustering of these 3 behaviors (called “sedentary lifestyle”), as well as leisuretime physical inactivity, with chronic diseases in Brazilian male and female workers.

Methods This study was derived from the “Lifestyle and Leisure Habits of Industrial Workers” survey, a cross-sectional study with employees of industrial companies from 23 states and the Federal District of Brazil (24 out of 27 federal units of the country) between 2006 and 2008. The states of Rio de Janeiro, Piaui, and Sergipe did not participate in the survey because data collection was not completed within the research schedule. In Brazil, industrial companies operate in the mining, processing, public utility, and construction sectors.8 During the study period, workers from these industries represented 24% of the formal workers in the country, which was equivalent to approximately 8.5 million people.9 Sample size calculation and sample planning were performed independently for each federal unit. The parameters used for calculating the sample size included the following: a leisure-time physical inactivity prevalence of 45%, which was obtained from an earlier study with industrial workers,10 a sampling error of 3 percentage points, and a 95% confidence interval. The sample size was increased by 50% because of the design effect and by 20% for missing data and survey refusals. The total sample required for all participating federal units (n = 24) was 52,774 workers.

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Sedentary Behavior, Inactivity, and Chronic Disease   1623

In the sampling strategy, the workers were first stratified by company size (number of employees): small (20 to 99 employees), medium (100 to 499 employees), or large (500 or more employees). Second, workers within each company size group were further stratified according to the geographic regions within the federal unit, maintaining the proportion found in the population. Third, companies were randomly selected according to size and geographic region. In each geographic region, 10% to 50% of companies of each size were selected according to the number of existing companies and the number of workers required for the sample. Companies that did not authorize their employees to participate in the survey were replaced by others of a similar size and type within the same geographic region. Finally, workers were selected within the sampled companies through a systematic sampling scheme using employee lists provided by the companies. All workers completed a questionnaire under interviewer supervision. All procedures were standardized and all interviewers were trained. The questionnaire was administered in the companies to groups of 3 to 15 workers, and 2 interviewers were always present in each occasion. The instrument was previously tested for its content, logic, clarity, and reliability.11 Two senior researchers carried out an instrument content and logic validation. A pilot test was performed to evaluate clarity, as well as detect and solve any problems. Reliability analyses showed Kappa and intraclass correlation coefficients ranging from 0.40 to 0.79 (moderate to strong agreement). Data were entered into the database through optical scanning using the Sphynx program (Sphynx Software Solutions Incorporated, Washington, US). Dependent variables in this study were hypertension, hypercholesterolemia, type 2 diabetes, obesity (BMI ≥ 30.0 kg/m2), and clustering of chronic diseases. Hypertension, hypercholesterolemia, and type 2 diabetes were obtained through the questions worded as follows: “Has any doctor, nurse or other health professional ever said that you have ?” The answer options were “Yes,” “No,” “I do not remember,” and “I never did a test to evaluate .” Unknown answers (the last 2 options) were excluded from analysis. Obesity (body mass index ≥ 30 kg/m2) was investigated based on self-reported weight and height (reported as continuous variables and having 1 decimal point). The clustering of chronic diseases was analyzed by summing the positive self-reported diagnosis (0, 1, or ≥ 2). The exposure variables were leisure-time physical inactivity, television viewing, transport mode to work, sedentary work (predominantly seated at work), and sedentary lifestyle. Leisuretime physical inactivity was defined as the negative response to the question: “Do you regularly perform some kind of physical activity in your leisure time, such as physical exercise (gymnastics, walking, running), sports, dance, or martial arts?” Television viewing was assessed by the question: “On average, how many hours do you spend watching television on a normal weekday?” The answers were categorized as ≤ 1, 2, 3, and ≥ 4 hours/day. Transport mode to work was assessed by the question: “How do you go to work on most days of the week?” The answer options were the categories walking or cycling, bus, and car or motorcycle. To assess sedentary work, participants were asked: “How would you describe your activities at work?” The answer options were: “I spend most of the time seated and, at most, walk short distances,” “I perform moderate activities, such as walking fast or performing manual tasks, for most of the day,” and “I frequently perform vigorous physical activities.” Subjects who answered the first option were categorized as sedentary at work. Finally, participants were classified as having a sedentary lifestyle when

they reported sedentariness in travel to work (ie, by car or motorcycle), in the working setting, and spent ≥ 2 hours/day of television viewing. The following variables were also included in the analyses: • Sociodemographic characteristics: federal unit, age in years (< 30, 30–39, or ≥ 40), educational level (elementary school, middle school, high school, or college) and monthly family income in US$ (≤ 280, 281–1340, or > 1340). • Lifestyle variables: fruit and vegetable consumption (< 5 or ≥ 5 days/week, for both fruits and vegetables12), smoking (yes or no, which included never smokers or former smokers), and excessive alcohol consumption (females with > 7 drinks and males with > 14 drinks during a normal week, or subjects of either gender having ≥ 5 drinks on 1 occasion within the last 30 days13). Descriptive statistics used absolute and relative frequencies and their respective 95% confidence intervals (95% CI). Binary logistic regression was used for all statistical analyses, except in the analysis of the clustering of chronic diseases in which multinomial logistic regression was performed. Analyses were stratified by gender (formal tests of interaction support stratification; data not shown). Results were expressed as odds ratios (OR) with their 95% CI and obtained with simple (crude OR) and multiple (adjusted OR) logistic regressions. Adjustment variables were organized into 3 levels: 1) sociodemographic characteristics; 2) lifestyle variables, leisuretime physical inactivity, and sedentary behaviors (except the same behavior analyzed); and 3) other chronic diseases. For statistical modeling, a backward selection strategy and a critical P-value ≤ 0.20 were used to retain variables in the model at each level. The effects of each independent variable were adjusted to other variables of the same and previous levels. Stata Standard Edition, version 11.0 for Microsoft Windows (StataCorp LP, College Station, TX, USA) was used for data analysis. The Research Ethics Committee of the Federal University of Santa Catarina, Brazil, approved the survey (protocols no. 306/05 and 009/2007). Before conducting the survey, workers were informed of the voluntary nature of their participation and the guaranteed confidentiality of their responses. Only those who consented to participate answered the questionnaire. The Brazilian Social Service for Industry, a partner in organizing the survey, authorized this data analysis.

Results A total of 47,886 workers and 2775 industrial companies participated in the survey. The mean questionnaire return rate was 90.6% (s = 8.6). Four hundred and nine subjects (0.9% of the total) were excluded because they had no response for gender. Therefore, data analysis was performed for 47,477 workers (69.8% male). Characteristics of the study population are shown in Table 1. Women had a higher prevalence of leisure-time physical inactivity, use of bus as transport mode to work, and sedentary work than men. In turn, the prevalence of ≥ 4 hours/day of television viewing, walking or cycling as transport mode to work, obesity, and clustering of ≥ 2 chronic diseases were greater in men. Sedentary lifestyle was more prevalent in women (n = 2282; 16.2%; 95% CI: 15.6%–16.8%) than men (n = 3344; 10.2%; 95% CI: 9.9%–10.5%). In men, behaviors positively associated with hypertension after full adjustment were leisure-time physical inactivity (OR = 1.18; 95% CI: 1.09–1.28) and sedentary work (OR = 1.10; 95%

Table 1  Sociodemographic Characteristics, Lifestyle Characteristics, and Chronic Diseases, by Gender Men Variables

Women

n

% (95% CI)

n

% (95% CI)

 ≥ 40

14,965 10,085 7976

45.3 (44.8, 45.8) 30.5 (30.0, 31.0) 24.2 (23.7, 24.6)

6836 4554 2869

47.9 (47.1, 48.8) 32.0 (31.2, 32.7) 20.1 (19.5, 20.8)

Educational level   Elementary school   Middle school   High school  College

7274 5666 16,372 3782

22.0 (21.5, 22.4) 17.1 (16.7, 17.5) 49.5 (48.9, 50.0) 11.4 (11.1, 11.8)

1695 1759 7801 3021

11.9 (11.3, 12.4) 12.3 (11.8, 12.9) 54.6 (53.8, 55.5) 21.2 (20.5, 21.8)

10,810 13,821

32.9 (32.4, 33.4) 42.1 (41.6, 42.6)

4259 5630

30.1 (29.3, 30.8) 39.7 (38.9, 40.6)

8187

25.0 (24.5, 25.4)

4274

30.2 (29.4, 30.9)

19,999 12,999

60.6 (60.1, 61.1) 39.4 (38.9, 39.9)

8004 6241

56.2 (55.4, 57.0) 43.2 (43.0, 44.6)

 ≥ 5

16,559 16,457

50.2 (49.6, 50.7) 49.8 (49.3, 50.4)

6045 8216

42.4 (41.6, 43.2) 57.6 (56.8, 58.4)

Smoking  No  Yes

28,020 5037

84.8 (84.4, 85.2) 15.2 (14.8, 15.6)

13145 1126

92.1 (91.7, 92.6) 7.9 (7.4, 8.3)

Excessive alcohol consumptiona  No  Yes

16,428 16,494

49.9 (49.4, 50.4) 50.1 (49.6, 50.6)

10759 3461

75.7 (75.0, 76.4) 24.3 (23.6, 25.0)

Leisure-time physical inactivity  No  Yes

20,150 12,799

61.2 (60.6, 61.7) 38.8 (38.3, 39.4)

5590 8593

39.4 (38.6, 40.2) 60.6 (59.8, 61.4)

4534 8872 9879 9648

13.8 (13.4, 14.1) 26.9 (26.5, 27.4) 30.0 (29.5, 30.5) 29.3 (28.8, 29.8)

2442 4345 3804 3597

17.2 (16.6, 17.8) 30.6 (29.9, 31.4) 26.8 (26.1, 27.5) 25.4 (24.6, 26.1)

Transport mode to work   Walking or cycling  Bus   Car or motorcycle

9633 14,706 8690

29.2 (28.7, 29.7) 44.5 (44.0, 45.1) 26.3 (25.8, 26.8)

3219 7490 3550

22.6 (21.9, 23.3) 52.5 (51.7, 53.3) 24.9 (24.2, 25.6)

Sedentary work  No  Yes

24,163 8843

73.2 (72.7, 73.7) 26.8 (26.3, 27.3)

6379 7817

44.9 (44.1, 45.8) 55.1 (54.2, 55.9)

Hypertensionb  No  Yes

25,053 4557

84.6 (84.2, 85.0) 15.4 (15.0, 15.8)

10978 1791

86.0 (85.4, 86.6) 14.0 (13.4, 14.6)

Hypercholesterolemiab  No  Yes

23,993 3321

87.8 (87.5, 88.2) 12.2 (11.8, 12.5)

10628 1468

87.7 (87.1, 88.3) 12.3 (11.7, 12.9)

Age (in years)  1340 Fruit consumption (days/week)   14 drinks during a normal week (for women and men, respectively) or ≥ 5 drinks on 1 occasion within the last 30 days for both genders. excluded from analyses (“I do not remember” or “I never did a test to evaluate ”): hypertension: n = 2380 (5.3%); hypercholesterolemia: n = 5345 (11.9%); type 2 diabetes: n = 5093 (11.4%).

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a

b Answers

CI: 1.01–1.20). However, 3 hours/day of television viewing was negatively associated with hypertension (OR3h = 0.86; 95% CI: 0.75–0.98). In women, only the use of bus as a transport mode to work was positively associated with hypertension (OR = 1.21; 95% CI: 1.03–1.41) (Table 2). All behaviors were positively associated with hypercholesterolemia in the crude analysis, except leisure-time physical inactivity and television viewing in women (Table 3). In the adjusted analysis, the following behaviors remained positively associated in the final model: sedentary work for men (OR = 1.34; 95% CI: 1.21–1.48) and women (OR = 1.15; 95% CI: 1.01–1.31); and leisure-time physical inactivity (OR = 1.18; 95% CI: 1.08–1.30) and sedentary lifestyle (OR = 1.35; 95% CI: 1.19–1.52) for men. For women, 2 and 3 hours/day of television viewing were negatively associated with hypercholesterolemia (OR2h = 0.79; 95% CI: 0.65–0.96; OR3h = 0.81; 95% CI: 0.67–0.99). An association was observed in neither gender between the analyzed behaviors and type 2 diabetes after full adjustment (Table 4). Leisure-time physical inactivity was positively associated with obesity in men (OR = 1.27; 95% CI: 1.16–1.39) and women (OR = 1.33; 95% CI: 1.11–1.58) after all adjustments. Staying predominantly seated at work was also positively associated with obesity in both genders (OR = 1.27; 95% CI: 1.15–1.41 in men; OR = 1.24; 95% CI: 1.04–1.48 in women). In men, bus (OR = 1.25; 95% CI: 1.11–1.42) and car or motorcycle as transport mode to work (OR = 1.62; 95% CI: 1.41–1.85) and sedentary lifestyle (OR = 1.44; 95% CI: 1.27–1.63) were positively associated with obesity in the final model. However, in women, the use of car or motorcycle as transport to work was negatively associated with obesity (OR = 0.74; 95% CI: 0.56–0.97) (Table 5). In men, all behaviors were positively associated with the clustering of chronic diseases in the crude analysis. Following all adjustments, men who reported leisure-time physical inactivity, use of bus, car, or motorcycle as transport to work, sedentary work, and sedentary lifestyle were more likely to have 1 and 2 or more chronic diseases than none (Table 6). This pattern was not seen in women. Considering the full model, women who reported ≥ 3 hours/day of television viewing were more likely to have 1 chronic disease than no chronic diseases, and those who used a bus as transport to work were more likely to have 2 or more chronic diseases than none (Table 7).

Discussion In this study, we seek to provide relevant and detailed information about the association between different types of sedentary behaviors and leisure-time physical inactivity with hypertension, hypercholesterolemia, type 2 diabetes, obesity, and clustering of chronic diseases. The prevalence of these chronic diseases were slightly lower in our sample than in the general Brazilian population.14 However, 1 in 4 Brazilian workers has at least 1 chronic disease, and these diseases are highly related to cardiovascular and all-cause mortality. The magnitude effect on the outcomes varied greatly across television viewing, sedentary work, transport mode to work, and sedentary lifestyle (ie, using a car or motorcycle to travel to work, predominantly seated during work time, and watching television ≥ 2 hours/day every day), as well as between genders. Sedentariness at work was the most consistent behavior positively associated with chronic diseases; associations were independent of sociodemographic characteristics, lifestyle (including leisure-time physical inactivity), and other chronic diseases, especially in men. Moreover, leisure-time physical inactivity was associated with all chronic diseases and with the clustering of diseases in men.

Leisure-Time Physical Inactivity and Chronic Diseases In this study, there was a positive association between leisure-time physical inactivity and the presence of hypertension and hypercholesterolemia among men. In all statistical models tested, these associations were not confirmed for women. This result seems to reinforce the finding that the etiology of chronic diseases differs between genders once social determinants may interact with biological predisposition to disease.15 Leisure-time physical inactivity was also positively associated with higher odds of obesity in both genders. This study presents cross-sectional design; therefore, it is not possible to infer causality in the association between physical inactivity and obesity. However, studies in other countries have also demonstrated that people with high levels of leisure-time physical activity have lower body mass index and waist circumference than their peers.16–18 These results indicate the importance of incorporating leisure-time physical activity into

1626

2120

 Yes

1209

1355

1390

 2

 3

 ≥ 4

1946

1395

 Bus

  Car or motorcycle

1445

 Yes

20.6

636

1.25 (1.17, 1.33)

1.42 (1.33, 1.51)

1.00 1.25 (1.13, 1.39)

1.50 (1.37, 1.65)

(1.12, 1.30)

(1.23, 1.41) 1.00

1.21

(0.99, 1.20)

(1.19, 1.40)

1.31

1.09

1.29

1.00

(0.91, 1.07)

(0.95, 1.11)

1.00

0.99

1.00

1.03

1.00

1.02 (0.91, 1.14)

(1.13, 1.39)

1.25

1.00

(1.11, 1.30)

1.20

1.00

(0.96, 1.17)

1.06

(0.91, 1.08)

0.99

1.00

(0.93, 1.17)

1.04

0.89 (0.79, 0.99)

0.87 (0.78, 0.97)

0.99 (0.89, 1.10) 1.06

0.88 (0.79, 0.99)

0.87 (0.78, 0.97)

1.01 (0.91, 1.13)

(0.96, 1.18)

1.00

(1.15, 1.32)

1.23

1.00

Model 2

1.00

1.00

1.00

Model 1

1.00

Crude

OR (95% CI)

(0.98, 1.24)

1.10

1.00

(1.01, 1.20)

1.10

1.00

(0.87, 1.09)

0.97

(0.83, 1.01)

0.92

1.00

(0.87, 1.13)

0.99

(0.75, 0.98)

0.86

(0.79, 1.03)

0.90

1.00

(1.09, 1.28)

1.18

1.00

Model 3

254

1507

945

833

387

999

399

492

513

511

260

1097

675

n

11.8

14.5

13.2

15.0

11.8

14.9

14.5

14.9

14.5

13.0

13.7

14.0

13.9

%

(0.69, 0.91)

0.79

1.00

(0.78, 0.95)

0.86

1.00

(0.68, 0.92)

0.79

(0.91, 1.17)

1.03

1.00

(0.94, 1.30)

1.11

(0.91, 1.27)

1.07

(0.80, 1.11)

0.94

1.00

(0.91, 1.12)

1.01

1.00

Crude

(1.08, 1.42)

(1.07, 1.40)

(0.75, 1.02)

0.87

1.00

(0.85, 1.06)

0.95

1.00

(0.84, 1.18)

(0.75, 1.02)

0.87

1.00

(0.85, 1.05)

0.94

1.00

(0.85, 1.19)

1.00

1.24 1.00

1.00 1.23

(0.97, 1.35)

1.14

(0.89, 1.25)

1.06

(0.76, 1.06)

0.90

1.00

(0.89, 1.10)

0.99

1.00

Model 2

1.00

(0.97, 1.35)

1.14

(0.89, 1.25)

1.06

(0.76, 1.06)

0.90

1.00

(0.88, 1.09)

0.98

1.00

Model 1

OR (95% CI)

Women

(0.71, 1.00)

0.84

1.00

(0.84, 1.08)

0.95

1.00

(0.81, 1.19)

0.98

(1.03, 1.41)

1.21

1.00

(0.98, 1.46)

1.20

(0.99, 1.46)

1.20

(0.86, 1.26)

1.04

1.00

(0.84, 1.07)

0.94

1.00

Model 3

a Yes

= ≥ 2 hours/day of television viewing, transport to work by car or motorcycle, and predominantly sitting at work. In Model 2, there is no adjustment for sedentary behaviors. Note. Model 1: adjusted for sociodemographic characteristics (federal unit, age, educational level, and monthly family income). Model 2: adjusted for model 1 plus lifestyle (fruit and vegetable consumption, smoking, excessive alcohol consumption, leisure-time physical inactivity, and sedentary behaviors—except the same behavior). Model 3: adjusted for model 2 plus other chronic diseases. Bold = significant OR.

14.7

3858

 Yesa

18.1

14.4

17.7

14.7

14.3

15.9

15.0

15.2

15.1

18.2

13.5

%

 No

Sedentary lifestyle

3098

 No

Sedentary work

1203

  Walking or cycling

Transport mode to work

563

 ≤ 1

Television viewing (hours/ day)

2411

n

 No

Leisure-time physical inactivity

Variables

Men

Table 2  Prevalence, Crude and Adjusted Odds Ratio (OR), and 95% Confidence Intervals (95% CI) of Hypertension, According to Leisure-Time Physical Inactivity and Sedentary Behaviors, by Gender

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1627

1513

Yes

872

1099

980

 2

 3

 ≥ 4

1270

  Car or motorcycle

23.4

692

(1.21, 1.41)

(1.30, 1.51)

1.00 1.45 (1.30, 1.61)

2.52 (2.29, 2.77)

(1.31, 1.55)

(1.95, 2.26) 1.00

1.00 1.43

1.22 (1.08, 1.37)

2.24 (2.02, 2.47) 1.00

(0.99, 1.22)

(1.27, 1.54)

2.10

1.00 1.10

1.00 1.40

1.04 (0.90, 1.19)

1.20

1.01 (0.88, 1.16)

1.31 (1.16, 1.49) (1.06, 1.37)

0.92 (0.80, 1.05)

1.17 (1.03, 1.34)

1.00

1.31

1.40

1.00

1.00

Model 1

1.00

Crude

(1.29, 1.60)

1.44

1.00

(1.30, 1.54)

1.41

1.00

(1.02, 1.29)

1.15

(0.98, 1.21)

1.08

1.00

(0.93, 1.23)

1.07

(0.91, 1.20)

1.05

(0.82, 1.08)

0.94

1.00

(1.18, 1.39)

1.28

1.00

Model 2

OR (95% CI)

(1.19, 1.52)

1.35

1.00

(1.21, 1.48)

1.34

1.00

(0.97, 1.27)

1.11

(0.95, 1.21)

1.07

1.00

(0.95, 1.28)

1.11

(0.97, 1.33)

1.13

(0.89, 1.23)

1.05

1.00

(1.08, 1.30)

1.18

1.00

Model 3

319

1144

942

536

453

786

244

412

409

424

228

869

605

n

15.3

11.6

13.8

10.3

14.4

12.3

9.6

13.3

12.1

11.4

12.6

11.9

12.9

%

(1.20, 1.57)

1.37

1.00

(1.24, 1.55)

1.39

1.00

(1.34, 1.87)

1.58

(1.13, 1.54)

1.32

1.00

(0.89, 1.26)

1.06

(0.80, 1.13)

0.95

(0.75, 1.05)

0.89

1.00

(0.82, 1.02)

0.91

1.00

Crude

(0.83, 1.13)

0.97

1.00

(1.04, 1.33)

1.18

1.00

(0.98, 1.41)

1.18

(1.11, 1.52)

1.30

1.00

(0.80, 1.16)

0.96

(0.68, 0.97)

0.81

(0.65, 0.94)

0.78

1.00

(0.86, 1.08)

0.97

1.00

Model 1

(0.83, 1.13)

0.97

1.00

(1.02, 1.31)

1.16

1.00

(0.94, 1.37)

1.14

(1.09, 1.50)

1.27

1.00

(0.80, 1.15)

0.96

(0.67, 0.97)

0.81

(0.65, 0.93)

0.78

1.00

(0.86, 1.09)

0.97

1.00

Model 2

OR (95% CI)

Women

(0.79, 1.11)

0.94

1.00

(1.01, 1.31)

1.15

1.00

(0.89, 1.33)

1.09

(0.99, 1.40)

1.18

1.00

(0.74, 1.11)

0.91

(0.67, 0.99)

0.81

(0.65, 0.96)

0.79

1.00

(0.84, 1.08)

0.95

1.00

Model 3

a Yes

= ≥ 2 hours/day of television viewing, transport to work by car or motorcycle, and predominantly sitting at work. In Model 2, there is no adjustment for sedentary behaviors. Note. Model 1: adjusted for sociodemographic characteristics (federal unit, age, educational level, and monthly family income). Model 2: adjusted for model 1 plus lifestyle (fruit and vegetable consumption, smoking, excessive alcohol consumption, leisure-time physical inactivity, and sedentary behaviors—except the same behavior). Model 3: adjusted for model 2 plus other chronic diseases. Bold = significant OR.

10.8

2600

 Yesa

18.5

9.8

17.1

11.4

8.5

12.2

13.1

11.9

10.3

14.4

10.7

%

 No

Sedentary lifestyle

1915

1399

 No

 Yes

Sedentary work

640

1401

  Walking or cycling

 Bus

Transport mode to work

354

 ≤ 1

Television viewing (hours/day)

1788

n

No

Leisure-time physical inactivity

Variables

Men

Table 3  Prevalence, Crude and Adjusted Odds Ratio (OR), and 95% Confidence Intervals (95% CI) of Hypercholesterolemia, According to Leisure-Time Physical Inactivity and Sedentary Behaviors, by Gender

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1628

339

 Yes

210

249

 3

 ≥ 4

318

241

 Bus

  Car or motorcycle

269

 Yes

1.00 1.24 (1.01, 1.53)

1.00 1.68 (1.38, 2.05)

1.21 (1.03, 1.43)

1.45 (1.25, 1.69)

(0.76, 1.16)

(1.05, 1.55) 1.00

0.94

1.28

1.00

0.95 (0.79, 1.15)

1.00 (0.84, 1.21)

1.00

(0.83, 1.37)

(0.92, 1.51) 1.00

1.06

(0.60, 1.00)

(0.74, 1.22) 1.18

0.78

(0.63, 1.06)

(0.80, 1.32) 0.95

0.82

1.03

1.00

1.16 (1.00, 1.35)

1.33 (1.15, 1.54)

1.00

1.00

(1.02, 1.56)

1.26

1.00

(1.06, 1.47)

1.25

1.00

(0.72, 1.12)

0.90

(0.78, 1.14)

0.94

1.00

(0.84, 1.39)

1.08

(0.60, 1.01)

0.78

(0.63, 1.05)

0.81

1.00

(1.02, 1.38)

1.18

1.00

Model 2

OR (95% CI) Model 1

1.00

Crude

(0.81, 1.29)

1.02

1.00

(0.88, 1.27)

1.06

1.00

(0.65, 1.05)

0.82

(0.72, 1.10)

0.89

1.00

(0.80, 1.40)

1.06

(0.60, 1.05)

0.79

(0.67, 1.18)

0.89

1.00

(0.94, 1.30)

1.11

1.00

Model 3

56

230

151

135

88

142

57

82

72

82

51

160

120

n

2.7

2.3

2.2

2.6

2.8

2.2

2.2

2.6

2.1

2.2

2.8

2.2

2.6

%

(0.86, 1.55)

1.15

1.00

(0.67, 1.08)

0.85

1.00

(0.89, 1.75)

1.25

(0.72, 1.35)

0.99

1.00

(0.66, 1.35)

0.94

(0.52, 1.09)

0.75

(0.54, 1.10)

0.78

1.00

(0.66, 1.07)

0.84

1.00

Crude

(0.84, 1.63)

1.17

1.00

(0.68, 1.13)

0.87

1.00

(1.01, 2.16)

1.47

(0.83, 1.59)

1.15

1.00

(0.64, 1.31)

0.91

(0.48, 1.00)

0.70

(0.51, 1.04)

0.73

1.00

(0.67, 1.08)

0.85

1.00

Model 1

(0.52, 1.17)

(0.48, 0.99)

(0.84, 1.63)

1.17

1.00

(0.66, 1.10)

0.85

1.00

(1.01, 2.17)

1.48

(0.84, 1.59)

1.15

1.00

(0.63, 1.30)

(0.90, 1.80)

1.27

1.00

(0.65, 1.13)

0.86

1.00

(0.96, 2.17)

1.44

(0.70, 1.41)

1.00

1.00

(0.70, 1.55)

1.05

0.78 0.91

(0.56, 1.25) 0.69

0.84

1.00

(0.64, 1.08)

0.83

1.00

Model 3

(0.51, 1.04)

0.73

1.00

(0.67, 1.09)

0.85

1.00

Model 2

OR (95% CI)

Women

a Yes = ≥ 2 hours/day of television viewing, transport to work by car or motorcycle, and predominantly sitting at work. In Model 2, there is no adjustment for sedentary behaviors. Note. Model 1: adjusted for sociodemographic characteristics (federal unit, age, educational level, and monthly family income). Model 2: adjusted for model 1 plus lifestyle (fruit and vegetable consumption, smoking, excessive alcohol consumption, leisure-time physical inactivity, and sedentary behaviors—except the same behavior). Model 3: adjusted for model 2 plus other chronic diseases. Bold = significant OR.

2.5 4.2

613

126

 No

3.5

2.4

3.2

2.6

2.5

3.1

2.5

2.7

2.6

2.7

2.4

%

 Yesa

Sedentary lifestyle

480

 No

Sedentary work

191

  Walking or cycling

Transport mode to work

90

199

 ≤ 1

 2

Television viewing (hours/ day)

408

n

 No

Leisure-time physical inactivity

Variables

Men

Table 4  Prevalence, Crude and Adjusted Odds Ratio (OR), and 95% Confidence Intervals (95% CI) of Type 2 Diabetes, According to Leisure-Time Physical Inactivity and Sedentary Behaviors, by Gender

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1629

1324

 Yes

849

837

 3

 ≥ 4

1047

  Car or motorcycle

1082

 Yes

551

1.00 1.60 (1.43, 1.79)

1.00 2.12 (1.91, 2.35)

1.50 (1.37, 1.63)

1.80

(1.48, 1.86)

(1.91, 2.36)

(1.66, 1.96)

1.66

2.12

1.00

(1.11, 1.37)

(1.22, 1.50)

1.00

1.00 1.24

1.00 1.36

0.98 (0.86, 1.12)

1.07 (0.94, 1.21)

0.92 (0.80, 1.04)

1.05

(0.79, 1.03)

(0.90, 1.16) (0.93, 1.20)

0.90

1.02

1.00

1.39 (1.29, 1.51)

1.47 (1.36, 1.59)

1.00

1.00

Model 1

1.00

Crude

(1.42, 1.78)

1.59

1.00

(1.29, 1.53)

1.40

1.00

(1.38, 1.74)

1.55

(1.08, 1.34)

1.20

1.00

(0.88, 1.15)

1.01

(0.81, 1.06)

0.93

(0.78, 1.03)

0.90

1.00

(1.25, 1.47)

1.36

1.00

Model 2

(1.27, 1.63)

1.44

1.00

(1.15, 1.41)

1.27

1.00

(1.41, 1.85)

1.62

(1.11, 1.42)

1.25

1.00

(0.83, 1.13)

0.97

(0.80, 1.08)

0.93

(0.79, 1.08)

0.92

1.00

(1.16, 1.39)

1.27

1.00

Model 3

122

649

460

316

176

414

188

194

223

227

133

513

262

n

5.6

5.8

6.2

5.3

5.2

5.8

6.2

5.7

6.2

5.5

5.8

6.3

4.9

%

(0.79, 1.17)

0.96

1.00

(1.01, 1.37)

1.18

1.00

(0.67, 1.02)

0.83

(0.78, 1.11)

0.93

1.00

(0.78, 1.23)

0.98

(0.86, 1.35)

1.08

(0.76, 1.19)

0.95

1.00

(1.12, 1.52)

1.30

1.00

Crude

(0.72, 1.12)

0.90

1.00

(1.04, 1.43)

1.22

1.00

(0.64, 1.04)

0.82

(0.81, 1.18)

0.98

1.00

(0.77, 1.22)

0.97

(0.82, 1.29)

1.03

(0.73, 1.14)

0.91

1.00

(1.11, 1.51)

1.29

1.00

Model 1

(0.72, 1.12)

0.90

1.00

(1.07, 1.46)

1.25

1.00

(0.61, 1.00)

0.79

(0.79, 1.16)

0.96

1.00

(0.79, 1.27)

1.00

(0.83, 1.31)

1.04

(0.74, 1.16)

0.92

1.00

(1.11, 1.52)

1.29

1.00

Model 2

OR (95% CI)

Women

(0.72, 1.15)

0.91

1.00

(1.04, 1.48)

1.24

1.00

(0.56, 1.02)

0.78

(0.73, 1.13)

0.91

1.00

(0.75, 1.28)

0.98

(0.79, 1.32)

1.02

(0.67, 1.14)

0.88

1.00

(1.11, 1.58)

1.33

1.00

Model 3

a Yes

= ≥ 2 hours/day of television viewing, transport to work by car or motorcycle, and predominantly sitting at work. In Model 2, there is no adjustment for sedentary behaviors. Note. Model 1: adjusted for sociodemographic characteristics (federal unit, age, educational level, and monthly family income). Model 2: adjusted for model 1 plus lifestyle (fruit and vegetable consumption, smoking, excessive alcohol consumption, leisure-time physical inactivity, and sedentary behaviors—except the same behavior). Model 3: adjusted for model 2 plus other chronic diseases. Bold = significant OR.

8.0 15.6

2272

 No

12.6

7.4

12.4

8.3

6.2

9.0

8.9

8.6

8.5

10.7

7.5

%

 Yesa

Sedentary lifestyle

1716

 No

Sedentary work

576

1173

  Walking or cycling

 Bus

Transport mode to work

370

738

 ≤ 1

 2

Television viewing (hours/ day)

1463

n

 No

Leisure-time physical inactivity

Variables

OR (95% CI)

Men

Table 5  Prevalence, Crude and Adjusted Odds Ratio (OR), and 95% Confidence Intervals (95% CI) of Obesity, According to Leisure-Time Physical Inactivity and Sedentary Behaviors, by Gender

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1630 9074

 Yes

6672 7351 7181

 2

 3

 ≥ 4

5972

 Bus

  Car or motorcycle

6028

 Yes

2052

785

4880

1761

3949

1766

2492

1451

777

1751

1552

690

2509

3182

n

23.5

16.6

19.9

16.3

20.3

17.0

15.1

17.6

17.7

17.5

15.2

19.6

15.8

%

(0.95, 1.16)

(1.11, 1.35)

1.00 1.39 (1.26, 1.54)

1.00 1.77 (1.62, 1.93)

1.20 (1.12, 1.29)

(1.16, 1.39)

(1.45, 1.69)

1.39

1.27

1.56

(1.30, 1.48)

(1.01, 1.19)

(1.10, 1.26)

1.00

1.09

1.18

1.00

1.00

1.00

1.14

1.05

1.22

(1.03, 1.26)

(0.93, 1.14)

(1.08, 1.32)

1.21

1.03

(1.10, 1.34)

1.00

1.19

1.23 (1.15, 1.31)

1.36 (1.29, 1.44) 1.00

1.00

Model 1

CI)b

1.00

Crude

OR (95%

1 chronic disease

(1.14, 1.42)

1.27

1.00

(1.09, 1.26)

1.17

1.00

(1.14, 1.36)

1.24

(1.02, 1.18)

1.10

1.00

(1.05, 1.29)

1.16

(0.96, 1.17)

1.06

(0.93, 1.14)

1.03

1.00

(1.14, 1.29)

1.21

1.00

Model 2

507

1977

1054

1451

952

1036

518

771

777

648

301

1216

1281

n

15.2

6.7

11.9

6.0

11.0

7.0

5.4

8.0

7.9

7.3

6.6

9.5

6.4

%

(2.53, 3.13)

2.81

1.00

(2.08, 2.46)

2.26

1.00

(2.11, 2.64)

2.36

(1.23, 1.53)

1.37

1.00

(1.10, 1.45)

1.26

(1.08, 1.43)

1.24

(0.99, 1.32)

1.14

1.00

(1.51, 1.78)

1.64

1.00

Crude

(1.52, 1.94)

1.72

1.00

(1.50, 1.83)

1.66

1.00

(1.26, 1.63)

1.44

(1.01, 1.29)

1.14

1.00

(0.96, 1.29)

1.11

(0.83, 1.12)

0.97

(0.76, 1.03)

0.89

1.00

(1.36, 1.62)

1.48

1.00

Model 1

OR (95% CI)b

≥ 2 chronic diseases

(1.36, 1.80)

1.57

1.00

(1.46, 1.78)

1.61

1.00

(1.16, 1.51)

1.32

(0.98, 1.25)

1.11

1.00

(0.99, 1.33)

1.15

(0.85, 1.15)

0.99

(0.77, 1.05)

0.90

1.00

(1.34, 1.60)

1.46

1.00

Model 2

b

a Yes

= ≥ 2 hours/day of television viewing, transport to work by car or motorcycle, and predominantly sitting at work. In Model 2, there is no adjustment for sedentary behaviors. OR and 95% CI derived from a multinomial logistic regression. Category of reference: 0 chronic diseases. Note. Model 1: adjusted for sociodemographic characteristics (federal unit, age, educational level, and monthly family income). Model 2: adjusted for model 1 plus lifestyle (fruit and vegetable consumption, smoking, excessive alcohol consumption, leisure-time physical inactivity, and sedentary behaviors—except the same behavior). Bold = significant OR.

76.7 61.4

22,521

 Yesa

68.2

77.7

68.7

76.0

79.6

74.4

74.4

75.2

78.1

70.9

77.9

%

 No

Sedentary lifestyle

18,763

 No

Sedentary work

7664 11,178

  Walking or cycling

Transport mode to work

3543

 ≤ 1

Television viewing (hours/day)

15,687

n

 No

Leisure-time physical inactivity

Variables

0 chronic diseases

Table 6  Prevalence, Crude and Adjusted Odds Ratio (OR), and 95% Confidence Intervals (95% CI) of Number of Chronic Diseases in Men, According to Leisure-Time Physical Inactivity and Sedentary Behaviors

Downloaded by University of California on 09/17/16, Volume 11, Article Number 8

1631

6539

 Yes

74.7

2688

 ≥ 4

2714

  Car or motorcycle

5908

 Yes

78.1

417

2093

1428

1109

618

1380

550

687

725

743

379

1569

958

n

18.3

17.8

18.3

17.4

17.4

18.4

17.1

19.1

19.1

17.1

15.5

18.3

17.1

%

1.16 (1.01, 1.34) 1.23 (1.06, 1.41)

1.30 (1.13, 1.49) 1.30 (1.14, 1.50)

(0.84, 1.09)

(0.93, 1.18)

1.00 0.95

1.05

1.00

1.03 (0.94, 1.14)

1.09

1.00

(0.99, 1.18)

1.00

(0.86, 1.15)

(0.92, 1.18)

(0.77, 1.07)

0.91

1.00

(0.93, 1.14)

1.03

1.00

(0.84, 1.12)

0.97

(0.99, 1.26)

1.00

(1.01, 1.27)

(1.00, 1.24) 1.04

1.00 1.12

1.00 1.13

(1.09, 1.45)

1.26

(1.03, 1.37)

1.19

(0.90, 1.18)

1.03

1.00

(0.98, 1.19)

1.08

1.00

Model 2

1.11

1.00

(0.90, 1.19)

(0.98, 1.29)

1.00 1.03

1.12

1.00

1.05 (0.96, 1.15)

1.08

1.00

Model 1

CI)b

(0.99, 1.18)

1.00

Crude

OR (95%

1 chronic disease

150

648

481

322

218

432

155

222

223

228

129

485

315

n

6.6

5.5

6.2

5.1

6.1

5.8

4.8

6.2

5.9

5.3

5.3

5.6

5.6

%

1.00 1.01 (0.81, 1.25)

1.00 1.22 (1.01, 1.47)

1.19 (1.01, 1.40)

1.25 (1.08, 1.45)

(0.97, 1.59)

(1.05, 1.61)

1.00

1.24

1.00

(1.09, 1.64) 1.30

1.33

1.00

(0.91, 1.47)

1.16

(0.79, 1.26)

1.00

(0.68, 1.09)

0.86

1.00

(0.87, 1.19)

1.02

1.00

Model 1

(1.02, 1.49)

1.23

1.00

(0.99, 1.55)

1.24

(0.94, 1.47)

1.17

(0.81, 1.27)

1.01

1.00

(0.88, 1.18)

1.02

1.00

Crude

OR (95% CI)b

≥ 2 chronic diseases

(0.65, 1.09)

0.84

1.00

(0.99, 1.38)

1.17

1.00

(0.94, 1.56)

1.21

(1.10, 1.66)

1.35

1.00

(0.92, 1.48)

1.16

(0.78, 1.25)

0.99

(0.69, 1.11)

0.88

1.00

(0.90, 1.23)

1.05

1.00

Model 2

b

a Yes

= ≥ 2 hours/day of television viewing, transport to work by car or motorcycle, and predominantly sitting at work. In Model 2, there is no adjustment for sedentary behaviors. OR and 95% CI derived from a multinomial logistic regression. Category of reference: 0 chronic diseases. Note. Model 1: adjusted for sociodemographic characteristics (federal unit, age, educational level, and monthly family income). Model 2: adjusted for model 1 plus lifestyle (fruit and vegetable consumption, smoking, excessive alcohol consumption, leisure-time physical inactivity, and sedentary behaviors—except the same behavior). Bold = significant OR.

76.7 75.2

9031 1715

 No

75.6

77.6

76.5

75.8

 Yesa

Sedentary lifestyle

4948

 No

Sedentary work

2514 5678

  Walking or cycling

 Bus

Transport mode to work

75.1

2856

 3

77.7

1934 3374

 ≤ 1 79.2

76.1

77.2

%

 2

Television viewing (hours/day)

4317

n

 No

Leisure-time physical inactivity

Variables

0 chronic diseases

Table 7  Prevalence, Crude and Adjusted Odds Ratio (OR), and 95% Confidence Intervals (95% CI) of Number of Chronic Diseases in Women, According to Leisure-Time Physical Inactivity and Sedentary Behaviors

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1632  Garcia et al

obesity prevention programs to help balance between energy intake and expenditure.

Downloaded by University of California on 09/17/16, Volume 11, Article Number 8

Television Viewing and Chronic Diseases The current study found that one-third of men and one-quarter of women workers spent at least 4 hours per day watching television. These findings are more extreme than the findings from the 2009 Brazilian Surveillance System of Risk and Protective Factors for Chronic Non-Communicable Diseases through Telephone Interviews (VIGITEL), which reported that approximately 26% of Brazilian adults (27.3% of men and 26.6% of women) spent 3 or more hours per day watching television;19 in our study, more than half of the sample of Brazilian workers watched this amount of television (59% of men and 51% of women). The high levels of television viewing observed in our study can be explained by the fact that workers can seek quieter leisure-time than the general population to compensate the physical fatigue during the workday.20 Television viewing is considered a major sedentary behavior and has been positively associated with several comorbidities and poor cardiovascular profile in both cross-sectional21,22 and prospective cohort studies.23 In this study, the odds of having 1 chronic disease compared with no chronic diseases was higher in workers with more hours of television viewing (≥ 4 and ≥ 3 hours/day for men and women, respectively) than in workers who watched up to 1 hour of television per day. However, there was no evidence of a positive association between television viewing and any particular chronic disease. Surprisingly, negative associations were found between television viewing and hypertension in men and between television viewing and hypercholesterolemia in women. The reason for these negative associations is not clear; however, there may have been measurement bias in the self-reporting of both television viewing and these health outcomes. On the other hand, other studies have also shown no evidence of a consistent positive association between time spent watching television and cardiovascular biomarkers,24–27 using different criteria or measures of television viewing and health outcomes.

Transport Mode to Work and Chronic Diseases Active commuting—commuting through the use of physical activity—is an important domain of physical activity and may contribute to individuals’ overall physical activity level.28 Active commuting has several advantages, such as low cost and potential to improve total daily physical activity. Despite these advantages, several studies have shown low levels of active commuting. The current study found that 3 out of 4 Brazilian workers reported motorized commuting to work. This proportion is far below the prevalence of active commuting in countries such as China;29 however, it seems to be consistent with the findings from research in high-income countries, such as the US.30 Prior studies that have prospectively investigated the association between active commuting with morbidity and mortality have found inconsistent findings. Hu and colleagues found a negative association between active commuting and several health outcomes in both healthy populations31,32 and populations with certain diseases.33,34 On the other hand, some studies have failed to demonstrate an independent effect of commuting on all-cause, cardiovascular, and cancer mortality.35 In this cross-sectional analysis, even after adjusting for sociodemographic variables, lifestyle variables and other chronic diseases, a positive association was found between motorized transport to

work and obesity among men and between motorized transport to work and hypertension among women. These findings support previous studies that showed independent and protective effects of active commuting on cardiovascular disease and other health outcomes.31,32,36 In addition, we found that men who use a car or motorcycle as a transport mode to work are more likely to have a cluster of chronic diseases. Thus, there is some evidence to promote active commuting as a health promotion strategy among workers and in the general population.

Sedentary Work and Chronic Diseases Researchers have shown that the time seated at work can be detrimental to health, increasing the likelihood of some chronic diseases37–40 and increasing all-cause mortality.37 This study pointed to a positive association between staying predominantly seated at work and both hypercholesterolemia and obesity in both genders. Findings from a longitudinal study conducted with adults in the UK corroborate these results and highlighted a negative association between time spent seated at work and HDL cholesterol levels and a positive correlation between time spent seated at work and the total concentration of triglycerides.38 With regard to obesity, studies in the US39 and Australia40 have found that workers who stay seated for long periods at work have a higher risk of total and central obesity. In this study, it was also found positive associations between sedentariness at work and both hypertension and clusters of chronic diseases in men. Staying predominantly seated at work has the most consistently positive association with chronic diseases in this population, especially in men. Therefore, given the effect of sedentary behavior on health and quality of life of employees, reduction of sitting time emerges as a feasible policy in work-related contexts.

Sedentary Lifestyle Epidemiological evidence suggests that there is a strong positive association between sedentary lifestyle and health outcomes.41 In this study, sedentary lifestyle has consistent positive association with chronic diseases among men. However, most studies have observed a positive association between time spent in sedentary behaviors and presence of chronic diseases in both genders; moreover, this positive association is often stronger in women than in men.27,42,43 On the other hand, some studies have also found no evidence of association between the time spent on sedentary activities and health outcomes in women.26,40 Some of the differences observed in the literature between countries may be due to the specific activities that were evaluated or the operational definitions used to characterize sedentary lifestyle. However, this discrepancy may also indicate that there are gender-related biological, social, or behavioral factors that have not yet been considered in existing studies. One possibility is that incidental daily activities related with socially defined roles for men and women—which are usually not measured—could alter the time each person spends engaging in sedentary activities each day. Few studies have tested the association between sedentary lifestyle and chronic disease clustering.27,42,43 The results of the current study reinforce the idea that sedentary lifestyle might not only be positively associated with isolated chronic diseases but also with a more negative overall cardiometabolic profile in men, which may result in increased risk for chronic disease and mortality. Moreover, the positive association between sedentary lifestyle and health outcome clustering appears to be independent of other lifestyle habits.

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Limitations and Positive Aspects of the Study Some limitations should be considered when interpreting the results of this study. First, a cause-effect relationship between the behaviors and the presence of chronic diseases is not possible because of the study design. Second, all measures of chronic diseases were selfreported. Despite being a widely used strategy for large-sample studies (such as the US’s National Health and Nutrition Examination Survey), results may be influenced by recall biases, social desirability, lack of knowledge about the existence of a disease, or lack of access or use of health care services. These biases could reduce sensitivity values, influencing (probably reducing, in this case) the association magnitude. Despite that, studies assessing the validity of self-reported chronic diseases in Brazilian adults addressing obesity and hypertension found acceptable diagnostic accuracy.44–46 Third, despite being commonly used to investigate the domains of a behavior, questionnaires may have reduced accuracy with respect to behaviors, as well as anthropometric measurements, which are used in the BMI calculation. Finally, the transport mode to work question does not account for distance or time, only type. In the future, it would be of interest to additionally test if there is a dose-response relationship between transport distance or time and chronic diseases. On the other hand, some positive aspects should be highlighted: 1) the quality, comprehensiveness and high response rate of the sample, which adequately represents workers from industrial companies in 24 of the 27 federal units in Brazil; 2) the possibility of performing analyses using models adjusted for sociodemographic characteristics, lifestyle characteristics, and other chronic diseases, which allowed for a more precise association estimation; and 3) the analysis of sedentary behaviors in different and combined domains.

Conclusion In summary, television viewing, sedentary work, passive transportation (car or motorcycle), and sedentary lifestyle had different magnitudes of association with chronic diseases. Sedentariness at work and leisure-time physical inactivity were the behaviors most consistently associated with chronic diseases in Brazilian workers, especially in men, and independently of sociodemographic characteristics, lifestyle, and the presence of other chronic diseases. Nonexercise, light-intensity and fidgeting-like physical activities present itself as links between sedentary behaviors and physical activity and, therefore, comprises one possible strategy to minimize the negative effects of this scenario. Inclusion of these activities into free-living contributes to reduce sedentary time, once people could move more, spend more energy and change between different body postures, and also collaborates increasing the physical activity level. Although these activities are performed at lower intensities, they could be accumulated for many hours during the day, presenting high volume and substantial energy expenditure. Interventions in companies could encourage these types of activities, reducing prolonged sitting time and promoting and allowing breaks in sedentary time among workers. For example, interrupting sitting time to engage in incidental activities (walking, fidgeting), postural activities (get up, stand up, stretching and body position change), and in workplace stretching or gymnastics programs, involving light-intensity activities. Such initiatives help to minimize damage to health due to the sedentary lifestyle, focusing on the main context where the sedentary behavior occurs to many adults. Finally, it is important to determine whether the differences between genders observed in this and other studies persist when using measures that are more accurate. Women tend to be more

health conscious and look for health care more frequently than men; therefore, we believe that chronic diseases prevalence would be even greater among men, probably raising the magnitude effect of physical inactivity and sedentary behavior in this population group. Overall, these findings reinforce the possibility that adverse health consequences are differently associated with sedentary behaviors according to domain and gender. Acknowledgments This study was derived from the “Lifestyle and Leisure Habits of Industrial Workers” survey, which was developed through a partnership with the Brazilian Social Service for Industry (SESI) and the authors’ research team. The survey received logistical and financial support from SESI.

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