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Ciência & Saúde Coletiva

Print version ISSN 1413-8123On-line version ISSN 1678-4561

Ciênc. saúde coletiva vol.25 no.7 Rio de Janeiro July 2020  Epub July 08, 2020

https://doi.org/10.1590/1413-81232020257.30082018 

FREE THEMES

Sedentary behavior across different domains among adult women in the south of Brazil: a population based study

Cristina Borges Cafruni1  2 
http://orcid.org/0000-0002-3568-0785

Maria Teresa Anselmo Olinto2 
http://orcid.org/0000-0002-3950-4594

Juvenal Soares Dias da Costa2 
http://orcid.org/0000-0003-3160-6075

Fernanda Souza de Bairros1  2 
http://orcid.org/0000-0002-8611-058X

Ruth Liane Henn2 
http://orcid.org/0000-0002-5056-4934

1Universidade Federal do Rio Grande do Sul. Av. Bento Gonçalves 9500, Agronomia. 91509-900 Porto Alegre RS Brasil. ccafruni@hotmail.com

2Universidade do Vale do Rio dos Sinos. São Leopoldo RS Brasil.


Abstract

The aim of this study was to describe sedentary behavior (SB) across leisure, occupation, and transport domains and determine factors associated with excessive sedentary behavior (ESB). Cross-sectional survey with a representative sample of 1,126 women aged 20-69 years living in São Leopoldo/RS. SB, demographic, socioeconomic, behavioral and health factors data were collected using a questionnaire administered by interviewers. The cut-off point for ESB was defined as the median. Associations were tested using Poisson regression with robust error variance. The medians and interquartile intervals (min/day) for leisure, occupation, and transport SB were 163.9(86.6-2710.5), 51.4(0-257.1), and 17.1(5.7-37.3), respectively. The likelihood of leisure SB increased with education level, was higher among women who were not employed, lived in household without children, and smokers. In other domains, there was an inverse association between age, being white, economic class, education level, and income and ESB. Direct association between living in a household with a car and excessive transport SB and women who were not employed were 30% less likely to engage in ESB in this domain. The predominant domain in Total SB was leisure. Associations differed across domains, indicating that domain-specific interventions should be implemented in addressing excessive SB.

Key words Sedentary lifestyle; Women’s health; Epidemiology

Resumo

O objetivo deste estudo foi descrever o comportamento sedentário (CS) nos domínios lazer, ocupação e deslocamento e verificar fatores associados ao excesso deste comportamento (ECS). Estudo transversal, com amostra representativa de 1.126 mulheres, 20-69 anos, de São Leopoldo/RS. CS, variáveis demográficas, socioeconômicas, comportamentais e relacionada à saúde foram avaliados através de questionário, aplicado em forma de entrevista. Considerou-se ECS valores acima da mediana. Utilizou-se regressão de Poisson com variância robusta. As medianas e intervalos interquartílicos (min/dia), para o CS no lazer, ocupação e deslocamento foram, respectivamente, 163,9 (86,6-2710,5), 51,4 (0-257,1) e 17,1 (5,7-37,3). A probabilidade do ECS no lazer aumentou com a escolaridade, foi maior entre as mulheres que não trabalhavam, sem crianças em casa e fumantes. Nos demais domínios, a probabilidade aumentou inversamente com a idade, foi maior entre mulheres brancas e aumentou com a classe econômica, escolaridade e renda. A probabilidade de ECS no deslocamento também aumentou com o número de carros no domicílio e foi 30% menor entre mulheres que não trabalhavam. O maior tempo de CS observado foi no domínio do lazer. As associações diferiram segundo o domínio, indicando distintas intervenções.

Palavras-chave Estilo de vida sedentário; Saúde das mulheres; Epidemiologia

Introduction

In recent decades, research examining the relationship between lifestyle and health has shown that sedentary behavior (SB) is a risk factor for morbidity and mortality1,2. SB comprises activity in a sitting or reclining posture while awake characterized by an energy expenditure of ≤ 1.5 metabolic equivalents (METs)3. This concept differs from physical inactivity, when an individual does not practice the amount of daily physical activity (PA) recommended by the World Health Organization4. Evidence from prospective studies shows that spending more time engaging in SB increases the risk of diabetes, cardiovascular disease, metabolic syndrome, and death5,6. Although high levels of moderate PA (~60-75 minutes per day) can eliminate or reduce some of these risks7, the majority of Brazil’s female population (91.5%)8 do not practice the minimum recommended amount of PA (~30 minutes per day of moderate-intensity physical activity)4.

Generally, it is the sum total of all types of sedentary behavior, or total sedentary behavior (Total SB), that determines health impact6. However, some studies suggest that certain types of SB may contribute to given morbidities more than others9,10. While assessing Total SB may help identify individuals or population groups at a higher risk11, this indicator alone is insufficient to plan interventions, because it does not provide an understanding of the sedentary activities that contribute most to excessive Total SB and the contexts in which it take place. It has therefore been suggested that studies examining SB should consider behavior across life domains, such as leisure, transport, and occupation12. This approach helps to understand both the composition of Total SB and associated factors, which may differ by domain13,14.

Few epidemiological studies have examined SB in Brazil8,15-18 and only one investigated behavior across different domains16. Little is known about the combined contribution of demographic, socioeconomic, behavioral, and health factors on the distribution of SB in population groups in developing countries. The survey “Condições de Vida e Saúde de Mulheres Adultas: Estudo de Base Populacional no Vale dos Sinos. Avaliação após 10 Anos” (The Living and Health Conditions of Adult Women: a Population-Based Study in Vale dos Sinos. A 10-Year Follow-up) provides an opportunity to investigate SB in this segment of the population. The objective of the present study was therefore to describe SB among these women across leisure, occupation, and transport domains (LSB, OSB, and TSB, respectively) and determine the factors associated with excessive sedentary behavior in each domain (ELSB, EOSB, and ETSB, respectively).

Methods

Conducted in 2015, the Living and Health Conditions of Adult Women survey is a cross-sectional study that investigated a representative sample of women aged between 20 and 69 years living in São Leopoldo. The following individuals were excluded: pregnant women; those mentally incapable of answering the questionnaire, based on the observations of the interviewer and confirmed by a person living in the household; and women unable to participate due to health reasons in the week prior to the interview.

São Leopoldo belongs to the Metropolitan Region of Porto Alegre and is located 33km from the capital of the State of Rio Grande do Sul. It has an area of 102,738 km2 and the population at the time of the 2010 Census was 214,087 inhabitants (population density 2060.31 inhabitants/km2) and showed a predominance of women (109,845)19. The Municipal Human Development Index in 2010 was 0.739, which is classified as high and above the national average (0.727)20.

The survey sample size was calculated for each of the various study outcomes, with the outcome “overdue cytopathologic test” and exposure variable “education level” resulting in the largest samples. Sample size was calculated considering a risk ratio of 2.0, 95% confidence level, 80% statistical power, and unexposed/exposed ratio of 1:2. The resulting sample size was increased by 10% to compensate for potential losses and refusals and 15% to control for confounding factors, resulting in a final sample of 1,281 women. The sample was selected using two-stage cluster sampling. In the first stage, 45 census tracts were randomly selected from the total number of tracts in the city (n = 371)19, followed by the random sampling of 36 households in each selected tract. Losses and refusals amounted to 12.1% of the sample, resulting in a final sample of 1,126 interviewed women. This number was used to calculate the power to detect the association between exposure variables and SB outcomes, resulting in a power of ≥ 70% to detect significant associations (prevalence ratio of ≥ 1.2) for exposure variables affecting between 33.6% and 78% of the sample, adopting a 95% confidence interval.

Sedentary behavior (SB) was measured using a questionnaire developed by the authors based on a review of the literature21,22 and an existing instrument16. The questionnaire was developed to enable the inclusion of sedentary activities in both sitting and reclining postures, in accordance with the definition of SB, and record activities on both weekdays and weekends. The following situations and/or places were included: a) computer, tablet or cellphone use for work/study at home; b) computer, tablet or cellphone use for leisure at home; c) watching TV; d) motorized transport (car, motorcycle, train, bus); e) the workplace, f) at college, on a course, or at university; g) drinking mate, h) visiting/seeing friends; and i) other activities (reading, religious activities, and manual activities). In the case of engagement in combined sedentary activities, the respondent was asked to report the main activity. Total SB and total leisure, occupation (including work and study), and transport SB (LSB, OSB, and TSB, respectively), expressed in minutes per day, were calculated considering different combinations of the situations and places outlined above: Total SB (all situations and places); LSB (b, c, g, and h); OSB (a, e, and f), and TSB (d). The totals were calculated based on the total amount of the time spent engaging in each activity each day divided by 7. The reliability of the questionnaire was tested using the test-retest method on sample of 97 study participants, resulting in correlation coefficients of 0.79, 0.79, and 0.82 (strong correlation) for Total SB, LSB, and OSB, respectively, and 0.60 (moderate correlation) for TSB.

For the independent variables we used a standardized, pre-coded, and pre-tested questionnaire based on different instruments administered by an interviewer. The questionnaire included the following demographic, socioeconomic, and behavioral variables: demographic variables - age (categorized in 10-year groups), skin color (white, non-white), marital status (with a partner, without a partner); socioeconomic variables - economic status (class A/B, C, D/E)23, education level (0 to 4, 5 to 7, 8 to 10, 11 to 14, ≥ 15 years), per capita family income (number of minimum salaries in quartiles), employed (yes; no); behavioral variables - smoking (non-smoker, smoker), alcohol intake (< 30g/day, ≥ 30g/day)24,25, leisure time PA (≥ 150 min/week, < 150 min/week)4, and transport PA (yes, no). For simplification purposes, the physical activity items covered only transport and leisure using questions adapted from the short International Physical Activity Questionnaire (IPAQ)26. Intensity of the activity (moderate/vigorous) was asked in the case of an affirmative answer. The questionnaire also included the following items: household cars (none; one; two or more); household computers (none; one; two or more); children in the household (none; one; two or more); and self-reported health (excellent/very good/good, fair/bad).

The interviews were conducted by trained interviewers who participated in the pilot study using a census tract not selected in the sampling phase. Data collection was conducted by a group of researchers over an 8-month period. Data quality was tested on a random sample of 10% of the respondents using a sample of the questions from the questionnaire with fixed responses in the short-term (age, number of children, etc.).

The study was approved by the Research Ethics Committee of the Vale do Rio dos Sinos University and all participants signed an informed consent form.

The data was double entered into the EpiData software version 3.1 to check and correct for possible entry errors. Descriptive analysis was performed using the IBM SPSS software version 22.0 (IBM Corp., Armonk, United States). Since the data were not normally distributed, they were described with medians and interquartile ranges. Excessive SB was defined using the corresponding median of each domain as a cut-off point. This procedure was adopted by previous studies because a SB threshold for health risk has not been established16,27. The data were expressed as percentages with their respective 95% confidence interval (95% CI). Since some women reported unreal Total SB values (> 24h/day), the maximum value was defined as 1,140 minutes per day, considering a minimum of 5 hours of sleep per day and values above this cut-off were replaced by the median (1.1% of the sample). Associations were tested using Poisson regression with robust error variance28 and the statistical software Stata MP 14.0 (Stata Corp., College Station, the United States). Variables that obtained p ≤ 0.20 in the bivariate analysis were included in the adjusted analysis, performed using a conceptual model comprising three levels of determination: level 1, including the demographic variables adjusted to each other; level 2, including the socioeconomic variables adjusted to each other and to the variables that obtained p ≤ 0.20 in level 1; and level 3, including the behavioral variables and household cars, children in the household, and self-reported health adjusted to each other and the variables that obtained p ≤ 0.20 in the previous levels. Marital status, employment, household cars, children in the household, self-reported health, and the behavioral variables were not included in the EOSB model because it is understood that these factors do not influence this outcome. Marital status and household computers were not tested in the ETSB model for the same reason. Variables with p < 0.05 were considered to be significantly associated with the outcome. In view of the sample design, the analyses were performed using Stata’s svy command.

Results

The mean age of the respondents was 43.3 years (SD±13.4) and the majority of the sample were white (74.4%), had a partner (63.8%), and lived in a household without a child (59.1%). Average education level was 9.8 years (SD±10.8) and the majority of the respondents worked (56%), had a per capita income of < 1.5 minimum salaries (74.7%), and belonged to economic class C (52.8%). The majority of the sample lived in households with at least one car and computer (62.4% and 63%, respectively). A large majority of respondents did not practice a minimum of 150 minutes of leisure time PA per week (85.7%). On the other hand, the majority of the sample did not report alcohol abuse (97.5%), were not smokers (81.5%), and reported excellent/very good/good self-reported health (66.3%) (Table 1).

Table 1 Characteristics of the study sample (n=1126). 

Variable n (%)
Age group (years)
60 to 69 163 14.5
50 to 59 228 20.2
40 to 49 275 24.4
30 to 39 244 21.7
20 to 29 216 19.2
Skin color
Non-white 288 25.6
White 838 74.4
Marital status
Without a partner 408 36.2
With a partner 718 63.8
Economic class
D and E 136 12.1
C 595 52.8
A and B 389 34.7
Education level (years)
0 to 4 204 18.1
05 to 07 253 22.5
08 to 10 197 17.5
11 to 14 360 32.0
15 + years 110 9.8
Income per capita in MS1
Up to 0.52 276 25.3
0.53 a 0.86 270 24.8
0.87 a 1.53 271 24.9
≥ 1.54 272 25.0
Employed
Yes 631 56.0
No 494 43.9
Household cars
None 417 37.0
One 547 48.6
Two or more 162 14.4
Household computers
None 423 37.6
One 461 40.9
Two or more 242 21.5
Children in the household2
Two or more 140 12.4
One 320 28.4
None 66 59.1
Smoking
Non-smoker 918 81.5
Smoker 208 18.5
Alcohol intake
< 30g/day 1098 97.5
≥ 30g/day 28 2.5
Leisure time PA
≥150 minutes per week 161 14.3
<150 minutes per week 965 85.7
Transport PA
Yes 744 66.1
No 382 33.9
Self-reported health
Excellent/very good/good 746 66.3
Fair/bad 380 33.7
Total 1126 100.0

1MS: minimum salaries (MS in 2015 = R$1,006.88);

2≤ 12 years; PA: Physical Activity.

The medians and interquartile ranges for Total SB, LSB, OSB, and TSB were 271.4 min/day (150.0-463.2), 163.9 min/day (86.8-270.5), 51.4 min/day (0-257, 1), and 17.1 min/day (5.7-37.3), respectively. The means and respective 95% confidence intervals for Total SB, LSB, OSB, and TSB were 319.4 min/day (306.8-331.9), 208.1 min/day (197.1-219.2), 141.5 min/day (95%CI: 128.7-154.4), and 33.1 min/day (95%CI: 29.8-36.2), respectively (data not shown in the Table).

The contribution of each domain to Total SB in the overall sample and by economic class is shown in Figure 1. This graph shows the mean SB values (min/day). In the overall sample, the predominant domain in Total SB was LSB (63%), followed by OSB (27%) and TSB (10%). There was no difference in the distribution of LSB across the different economic classes (p ≥ 0.05). However, the percentage contribution of LSB to Total SB in each class varied, accounting for 82% of Total SB in class D/E and 52% in class and A/B. There was a statistically significant difference in the distribution of OSB across classes (p < 0.001) and in the percentage contribution of OSB to Total SB in each class. The domain that contributed least to Total SB in each category was TSB (ranging from 9 to 11%). Although the percentage contributions of TSB to Total SB in each class were similar, there was a difference in the distribution of TSB across the classes (p < 0.001).

Figure 1 Mean time spent engaging in SB and percentage contribution of each domain to overall SB in the overall sample and by economic class.* p < 0,001; ** p > 0,05 (difference in distribution of SB tested using the Kruskal-Wallis test). 

Table 2 shows that the variables that showed an association with ELSB after control for confounding factors were education level, employment, children in the household, and smoking. Respondents with 15 years or more of education were 30% more likely to show ELSB than those with 0 to 4 years of study, while women who did not work were 60% more likely to show ELSB than those who did. Respondents without children in the household and smokers were 40% and 30% more likely, respectively, to show ELSB (95%CI: 1.1-14).

Table 2 Prevalence, crude and adjusted prevalence ratios (PR) and 95% confidence interval (95% CI) of excessive leisure sedentary behavior by sample characteristics (n=1126). 

Variable Prevalence PR crude p-value Adjusted PR p-value
% (95% CI) (95% CI) (95%CI)
Level 1
Age group (years)
60 to 69 58.9 (49.2-68.6) 1 0.28* § §
50 to 59 52.6 (45.2-60.0) 0.9 (0.7-1.1)
40 to 49 40.0 (33.2-46.8) 0.7 (0.5-0.8)
30 to 39 44.3 (37.1-51.4) 0.8 (0.6-0.9)
20 to 29 59.7 (53.1-66.3) 1.0 (0.8-1.2)
Skin color
Non-white 48.6 (43.1-54.4) 1 0.51** § §
White 50.5 (46.5-54.5) 1.0 (0.9-1.7)
Marital status 0.67**
Without partner 51.0 (46.0-56.0) 1 § §
With partner 49.4 (44.3-54.6) 1.0 (0.8-1.1)
Level 2
Economic status
D and E 43.4 (37.4-49.4) 1 0.13* 1
C 50.4 (44.8-56.0) 1.2 (1.0-1.4) 1.2 (1.0-1.4) 0.195*
A and B 51.9 (46.4-57.4) 1.2 (1.0-1.5) 1.2 (0.9-1.5)
Education level (years)
0 to 4 48.5 (42.0-55.1) 1 0.13* 1
05 to 07 47.4 (41.6-53.2) 1.0 (0.8-1.2) 1.0 (0.9-1.2) 0.040*
08 to 10 50.3 (42.0-58.6) 1.0 (0.9-1.3) 1.1 (0.9-1.4)
11 to 14 51.1 (45.4-56.7) 1.1 (0.9-1. 2) 1.1 (0.9-1.3)
15 54.5 (46.0-63.1) 1.1 (0.9-1.4) 1.3 (1.1-1.7)
Income in MS1
Up to 0.52 47.5 (41.4-53.5) 1 0.21* § §
0.53 to 0.86 48.5 (42.0-55.0) 1.0 (0.9-1.2)
0.87 to 1.53 51.3 (43.7-58.9) 1.1 (0.9-1.3)
≥ 1.54 52.9 (46.0-59.9) 1.1 (0.9-1.3)
Work
Yes 40.6 (36.0-45.2) 1 < 0.001** 1 < 0.001**
No 61.9 (56.8-67.0) 1.5 (1.3-1.8) 1.6 (1.4-1.9)
Level 3
Household cars
None 50.1 (45.5-54.0) 1 0.91* § §
One 50.1 (44.8-55.3) 1.0 (0.9-1.1)
Two or more 49.4 (39.8-58.9) 1.0 (0.8-1.2)
Household computers
None 47.3 (42.8-51.7) 1 0.11* 1 0.325*
One 50.1 (44.7-55.5) 1.1 (0.9-1.2) 1.0 (0.9-1.2)
Two or more 54.5 (47.1-62.0) 1.5 (1.0-1.4) 1.1 (0.9-1.4)
Children in the household2
Two or more 41.4 (30.3-52.5) 1 < 0.001* 1 0.001*
One 40.6 (34.4-46.8) 1.0 (0.7-1.4) 1.0 (0.7-1.4)
None 56.3 (52.0-60.6) 1.4 (1.0-1.8) 1.4 (1.0-1.8)
Smoking
Non-smoker 48.5 (44.5-52.5) 1 < 0.001 1 <0.001**
Smoker 56.7 (50.9-62.6) 1.2 (1.0-1.3) 1.3 (1.1-1.4)
Alcohol intake
< 30g/day 49.7 (46.0-53.5) 1 0.23** § §
≥ 30g/day 60.7 (41.2-80.2) 1.2 (0.9-1.7)
Leisure time PA
≥150 minutes per week 52.6 (45.7-59.5) 1 0.30** § §
<150 minutes per week 49.2 (45.5-52.9) 0.9 (0.8-1.1)
Transport PA
Yes 49.3 (44.9-53.7) 1 0.49** § §
No 51.3 (46.2-56.4) 1.0 (0.9-1.2)
Self-reported health
Excellent/very good/good 48.3 (44.1-52.4) 1 0.03** 1 0.489**
Fair/bad 53.4 (48.9-58.0) 1.1 (1.0-1.2) 1.0 (0.9-1.1)
Total 50.0 (46.3-53.7)

1SM: minimum salaries (MS in 2015 = R$1,006.88);

2≤12 years; PA: physical activity;

*Wald test for linear trend;

**Wald test to test for heterogeneity of the portions;

Each variable adjusted for the variables from the same and previous levels. Only variables with p ≤ 0.20 were included;

§ Variables with p ≤ 0.20 in the crude analysis.

All the variables tested in the adjusted analysis except household computers showed a statistically significant association with EOSB. The data also showed an inverse linear association between age group and EOSB and a direct linear association between socioeconomic class, education level, and income and the outcome. White women were 40% more likely than non-whites to show EOSB (Table 3).

Table 3 Prevalence, crude and adjusted prevalence ratios (PR) and 95% confidence interval (95% CI) of excessive occupation sedentary behavior by sample characteristics. (n=1126). 

Variable Prevalence Crude PR p-value Adjusted PR p-value
% (95% CI) (95% CI) (95% CI)
Level 1
Age group (years)
60 to 69 42.9 (26.2-59.5) 1 < 0.001* 1 < 0.001*
50 to 59 35.7 (24.8-46.7) 0.8 (0.5-1.3) 0.8 (0.5-1.3)
40 to 49 45.2 (38.3-52.2) 1.1 (0.7-1.5) 1.1 (0.7-1.5)
30 to 39 51.7 (44.0-59.5) 1.2 (0.8-1.8) 1.2 (0.8-1.8)
20 to 29 66.0 (57.3-74.6) 1.5 (1.0-2.3) 1.5 (1.0-2.4)
Skin color
Non-white 38.3 (31.9-44.8) 1 < 0.001* 1 < 0.001**
White 52.9 (47.1-58.7) 1.4 (1.2-1.6) 1.4 (1.2-1.6)
Marital status
Without partner 50.4 (43.1-57.6) NA NA
With partner 48.7 (43.3-54.1)
Level 2
Economic status
D and E 19.4 (12.1-26.7) 1 < 0.001* 1 0.033*
C 41.8 (35.8-47.8) 2.2 (1.5-3.2) 1.4 (0.9-2.1)
A and B 66.2 (60.2-72.1) 3.4 (2.3-5.1) 1.6 (1.0-2.6)
Education level (years)
0 to 4 24.7 (15.9-33.5) 1 < 0.001* 1 < 0.001*
05 to 07 25.5 (18.2-32.9) 1.0 (0.7-1.6) 0.9 (0.6-1.4)
08 to 10 40.0 (31.4-48.6) 1.6 (1.0-2.7) 1.3 (0.8-2.1)
11 to 14 65.2 (59.1-71.3) 2.6 (1.8-3.9) 1.7 (1.1-2.5)
15 78.0 (69.8-86.2) 3.2 (2.2-4.5) 1.7 (1.1-2.6)
Income in MS1
Up to 0.52 28.4 (22.5-34.2) 1 < 0.001* 1 < 0.001*
0.53 to 0.86 36.2 (30.2-42.3) 1.3 (1.0-1.7) 1.2 (0.9-1.6)
0.87 to 1.53 51.6 (44.1-59.2) 1.8 (1.4-2.3) 1.4 (1.0-1.8)
≥ 1.54 71.5 (64.5-78.5) 2.5 (1.9-3.2) 1.7 (1.3-2.2)
Work
Yes 51.3 (45.8-56.9) NA NA
No 25.9 (14.7-37.1)
Level 3
Household cars
None 36.3 (29.9-42.7) NA NA
One 53.2 (47.2-59.3)
Two or more 63.6 (53.7-73.4)
Household computers
None 31.4 (25.4-37.5) 1 < 0.001* 1 0.338*
One 51.4 (44.3-58.5) 1.6 (1.3-2.1) 1.1 (0.8-1.4)
Two or more 69.4 (62.3-76.4) 2.2 (1.8-2.7) 1.1 (0.9-1.5)
Children in the household2
Two or more 33.7 (22.6-44.9) NA NA
One 45.7 (39.6-51.8)
None 54.6 (47.3-61.8)
Smoking
Non-smoker 52.3 (46.7-57.9) NA NA
Smoker 35.8 (28.4-43.2)
Alcohol intake
< 30g/day 49.5 (44.1-54.9) NA NA
≥ 30g/day 44.4 (22.8-66.1)
Leisure time PA
≥150 minutes per week 66.3 (57.4-75.0) NA NA
<150 minutes per week 43.8 (38.8-48.8)
Transport PA
Yes 46.1 (40.7-51.5) NA NA
No 55.1 (46.1-64.0)
Self-reported health
Excellent/very good/good 54.0 (48.4-59.6) NA NA
Fair/bad 35.3 (28.1-42.5)
Total 49.3 (44.0-54.7)

1SM: minimum salaries (MS in 2015 = R$1,006.88);

2≤12 years; PA: physical activity; NA: Not Analyzed;

*Wald test for linear trend;

**Wald test to test for heterogeneity of the portions;

Each variable adjusted for the variables from the same and previous levels. Only variables with p ≤ 0.20 were included.

All the variables tested in the adjusted analysis except smoking, leisure time PA, transport PA, and self-reported health were significantly associated with ETSB (Table 4). The data also showed that the likelihood of ETSB decreases with age, while white women were 20% more likely than non-whites to show ETSB. There was a direct linear association between socioeconomic class, education level, and income and ETSB. Women who did not work were 30% less likely to show ETSB than those who work (95% CI: 0.6-08). The likelihood of ETSB increased with the number of cars in the household.

Table 4 Prevalence, crude and adjusted prevalence ratios (PR) and 95% confidence interval (95% CI) of excessive transport sedentary behavior by sample characteristics. (n=1126). 

Variable Prevalence Crude PR p-value Adjusted PR p-value
% (95% CI) (95% CI) (95% CI)
Level 1
Age group (years)
60 to 69 37.4 (30.3-44.6) 1 <0.001* 1 <0.001*
50 to 59 38.6 (31.2-46.0) 1.0 (0.8-1.3) 1.0 (0.8-1.3)
40 to 49 53.5 (47.1-59.8) 1.4 (1.2-1.7) 1.4 (1.2-1.8)
30 to 39 54.1 (46.9-61.3) 1.5 (1.2-1.8) 1.4 (1.2-1.8)
20 to 29 57.4 (51.4-63.4) 1.5 (1.3-1.9) 1.5 (1.3-1.9)
Skin color
Non-white 43.1 (36.6-49.5) 1 0.064** 1 0.046**
White 51.1 (45.9-56.2) 1.2 (1.0-1.4) 1.2 (1.0-1.4)
Marital status
Without partner 48.0 (43.2-52.9) NA NA
With partner 49.6 (44.6-54.6)
Level 2
Economic status
D and E 5.7 (0.0-19.0) 1 <0.001* 1 0.018*
C 14.3 (5.0-30.7) 1.7 (1.3-2.3) 1.3 (1.0-1.8)
A and B 28.6 (12.9-55.7) 2.5 (1.9-3.4) 1.5 (1.0-2.1)
Education level (years)
0 to 4 27.0 (21.2-32.7) 1 <0.001* 1 <0.001*
05 to 07 39.5 (33.7-45.3) 1.5 (1.1-1.9) 1.2 (0.9-1.7)
08 to 10 40.6 (32.0-49.2) 1.5 (1.1-2.0) 1.2 (0.9-1.6)
11 to 14 64.4 (58.8-70.2) 2.4 (1.9-3.0) 1.6 (1.2-2.1)
15 77.3 (69.2-85.4) 2.9 (2.3-3.6) 1.6 (1.2-2.2)
Income in MS1
Up to 0.52 33.0 (28.0-38.0) 1 <0.001* 1 0.006*
0.53 to 0.86 44.1 (45.1-60.4) 1.3 (1.1-1.6) 1.2 (1.0-1.5)
0.87 to 1.53 52.8 (45.1-60.4) 1.6 (1.3-1.9) 1.2 (1.0-1.5)
≥ 1.54 68.0 (60.7-75.3) 2.1 (1.7-2.5) 1.4 (1.1-1.7)
Work
Yes 61.2 (56.4-66.0) 1 <0.001* 1 <0.001*
No 33.6 (29.2-38.0) 0.6 (0.5-0.6) 0.7 (0.6-0.8)
Level 3
Household cars
None 34.3 (29.6-39.0) 1 <0.001* 1 <0.001*
One 53.4 (48.7-58.1) 1.6 (1.3-1.8) 1.2 (1.0-1.5)
Two or more 72.2 (62.2-82.3) 2.1 (1.8-2.5) 1.5 (1.2-1.8)
Household computers
None 36.2 (31.7-40.7) NA NA
One 49.7 (45.0-54.4)
Two or more 70.2 (61.7-78.8)
Children in the household2
Two or more 43.6 (32.7-54.4) 1 0.751* § §
One 52.5 (47.5-57.5) 1.2 (0.9-1.6)
None 48.5 (43.4-53.6) 1.1 (0.9-1.4)
Smoking
Non-smoker 51.0 (46.2-55.9) 1 0.016** 1 0.738**
Smoker 40.4 (33.9-46.8) 0.8 (0.7-1.0) 1.0 (0.8-1.1)
Alcohol intake
< 30g/day 48.8 (44.6-53.1) 1 0.227** § §
≥ 30g/day 57.1 (42.6-71.7) 1.2 (0.9-1.5)
Leisure time PA
≥ 150 minutes per week 56.3 (49.6-63.1) 1 0.002** 1 0.697**
< 150 minutes per week 46.7 (42.6-50.8) 0.8 (0.7-0.9) 1.0 (0.9-1.1)
Transport PA
Yes 45.8 (41.9-49.7) 1 0.008** 1 0.536**
No 55.2 (47.9-62.6) 1.2 (1.1-1.34) 1.0 (0.9-1.1)
Self-reported health
Excellent/very good/good 55.0 (49.6-60.3) 1 <0.001** 1 0.668**
Fair/bad 37.4 (33.4-41.4) 0.7 (0.6-0.8) 1.0 (0.9-1.1)
Total 49.0 (44.8-53.2)

1SM: minimum salaries (MS in 2015 = R$1,006.88);

2≤12 years; PA: physical activity; NA: Not Analyzed;

*Wald test for linear trend;

**Wald test to test for heterogeneity of the portions;

Each variable adjusted for the variables from the same and previous levels. Only variables with p ≤ 0.20 were included.

§ Variables with p ≤ 0.20 in the crude analysis.

Discussion

Half of the overall sample reported that they spent at least 270 minutes engaging in SB (equivalent to 4.5 hours per day), with LSB, OSB and TSB accounting for 63%, 27%, and 10% of Total SB, respectively. Certain variables were associated with all three domains, while others were domain-specific.

Both the median and mean values of Total SB observed in the present study (271.4 min/day; IQR: 150.0-463.2 and 319.4 min/day; 95% CI: 306.8-331.9) were similar to the findings of a study of a female sample in Pelotas16 (median: 240 min/day, IQR:135.0-480; mean: 323 min/day, 95% CI: 305,0-341.0). The respondents of the present study spent a mean of 50 more minutes a day engaging in SB than the women of a study in Ribeirão Preto18 (mean: 270.3 min/day; 95% CI: 256.3-284.23). However, these comparisons should be treated with caution since the studies used different instruments to measure SB12. For example, the lower values among women in Ribeirão Preto may be partially explained by the use of the IPAQ, which contains only one question on time spent sitting, thus leading to a possible underestimation of total sitting time18. In addition, the IPAQ does not consider time spent in engaging in TSB. The higher values observed by the present study may also be due to the fact that our instrument assessed SB in both sitting and reclining postures, while the studies mentioned above included only sitting posture.

The data presented show that half of the respondents reported spending at least 163.9 min/day engaging in LSB. The lack of studies using the same approach as the present study (for example, the inclusion of TV watching and leisure time computer use) hampers comparisons with national averages. Although our findings demonstrate that watching TV is the most frequent LSB, national data show a declining trend in the time spent on this activity in recent years17.

Half of the respondents who worked or studied (n = 685) spent at least 50 min/day engaged in workplace and/or study sedentary activities. The mean values for these activities (141.5 min/day; 95% CI: 121.68-161.41) were similar to those reported by the study conducted in Pelotas (~150 min/day; 95% CI: 130-165)16. Furthermore, the respondents of the present study spent a mean of 10 minutes more a day in TSB than the participants of the study in Pelotas (33 min/day; 95% CI: 29.8-36.2 compared to ~45 min/day; 95% CI: 40-50)16. All the women in the sample were included in this domain, considering that for the purposes of this study transport includes travel to work and for study and leisure purposes. However, occupation can have a significant influence on time spent engaging in TSB. The fact that a large part of respondents were not working at the time of the study may therefore have contributed to the low values found for this domain. Though not investigated in this study, it is also possible that the city’s characteristics may facilitate other forms of transport, such as walking or bicycle, reducing time spent on motorized transport due to the shorter distances travelled between places29,30.

The data presented show that the domain that contributed most to Total SB in the overall sample was LSB (63% do Total SB). This finding is important because LSB is the domain that has the greatest potential for reduction31, which is desirable considering the increased risk of morbidity and mortality associated with SB5,6. Evidence from studies examining cardiometabolic risk markers show that reallocating 30 minutes of sedentary time with either light-intensity physical activity or moderate to vigorous physical activity may be beneficial for health32. In the present study, this would require a 20% reduction in the time spent engaging in LSB. Figure 1 also shows that mean domain values and their respective percentage contribution to Total SB may vary according to exposure variable. We chose economic class to demonstrate these differences because socioeconomic inequalities can influence SB in different situations: at home, through the use of appliances and devices that can save time spent on household chores and promote leisure time sedentary activities (internet, computers, etc.); in the workplace, where new technologies mean work activities are performed in a sitting position; and transport, though access to motorized transport33. Despite methodological differences in SB assessment, our results are similar to the findings of the Pelotas study16, which showed a reduction in the percentage contribution of OSB and an increase in the percentage contribution of LSB to Total SB with decreasing economic status and similar percentage contributions of TSB across the three economic classes. One possible explanation for the differences in the percentage contributions of OSB and LSB across economic classes is that women with lower economic status tend to work in more physically demanding occupations and therefore spend less time in a sitting posture at work. However, although our findings show a large difference in the percentage contribution of LSB to Total SB in each economic class, there was no significant difference in the distribution of LSB across the classes. These findings are important because they show that interventions should be domain-specific34 and tailored to the specific characteristics of the target population.

The results of the adjusted analysis showed a direct linear association between education level and ELSB. This finding is consistent with the literature that looked at leisure time sedentary activities beyond time spent watching TV27,35. It is therefore likely that the respondents with a higher level of education level performed other types of leisure time sedentary activities. Our study showed a positive association between not working and lower number of children in the household and ELSB, which suggests that women with more spare time spend this time on leisure time sedentary activities36. Our findings show that smokers were more likely to show ELSB, suggesting clustering of unhealthy behaviors37. Although there is no evidence on which types of interventions are most effective in reducing ELSB38, our findings suggest that actions should include the promotion of healthy lifestyles and raising awareness about the danger of spending excessive time engaging in SB39,40.

The data presented show an inverse linear association between age and EOSB. This association may be partially explained by the fact that OSB included time spent sitting for study, which tends to be greater in younger women. However, studies that assessed only work SB reported similar findings41. The results of the final model also showed an association between OSB and skin color, revealing that white women were 38% more likely to show EOSB than non-whites. A study assessing occupational differences and skin color showed that white women are more likely to work in administrative or technical activities than non-whites42. All the socioeconomic variables were directly associated with EOSB, regardless of skin color, which is consistent with the findings of other studies41,43. Although not assessed by the present study, other studies have reported that working in certain types of occupations such as administrative, office, and service jobs increases the likelihood of high levels of SB, compared to more physically demanding occupations44,45. These studies suggest that women who show EOSB would benefit from workplace interventions, such as the use of workstations that create a variation in sitting postures46.

All the sociodemographic variables analyzed in the adjusted model showed an association with ETSB in the same direction as the associations with EOSB. Other variables that maintained a direct association with ETSB in the final model were working and having a household car. We did not find studies examining this domain with female-only samples, thus limiting comparisons. Further research is needed to determine whether having access to environments that provide favorable conditions for walking and cycling reduces ETSB among women47.

One of the strengths of the present study is that it is a representative population-based study, meaning that the results can be extrapolated to the female population of São Leopoldo. Furthermore, SB was measured across various activities taking into account both weekdays and weekends. Finally, our analysis considered three different SB domains and a wide range of exposure variables.

Limitations include the fact that cross-sectional studies are limited in their ability to determine the cause-and-effect relationship between variables. Another limitation is the fact that the validity of the questionnaire used was not tested, thus leading to the possibility of over or underestimation of SB. It is also possible that some combined sedentary activities were doubly reported, overestimating the time spent engaging in Total SB. Finally, despite using medians to describe the characteristics of women engaging in ESB, these values do not necessarily represent a health risk. Evidence shows that the risk of all-cause mortality among adults increases when Total SB exceeds 7 hours per day48. Based on this finding, one-quarter of the sample of the present study have an increased risk of mortality, showing a minimum of 7.7 hours per day spent on SB. Thus, the use of the median may attenuate the measures of effect. However, to the best of our knowledge, criteria for classifying ESB in each domain have not been established.

LSB was the predominant domain in Total SB. The percentage contribution of each domain and distribution of SB varied according to economic class. The findings showed an association between demographic variables and EOSB and ETSB, but no association was found with ELSB. At least one socioeconomic variable was positively associated with the outcome in each domain. Having a job was the only variable that showed an association in opposite directions, being directly associated with ELSB and inversely associated with ETSB. In addition, having a household car and not living in a household without a child directly influenced SB in two domains. Of all the behavioral variables analyzed, only smoking was associated with ELSB.

The findings of this study contribute to the identification of women at greater risk of engaging in excessive SB and defining appropriate interventions to reduce SB in each domain. Future studies should examine social, political, and environmental variables to obtain a deeper understanding of the factors influencing SB.

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Received: July 14, 2018; Accepted: November 05, 2018; Published: November 07, 2018

Collaborations

CB Cafruni reviewed the literature, was responsible for study conception and design and the development of the data collection instrument, trained the interviewers, supervised field work, and participated in data analysis and the writing of this article. MTA Olinto coordinated research and contributed to the drafting of the article. JSD Costa coordinated research and contributed to the drafting of the article. FS Bairros contributed to project conception and design, the development of the data collection instrument, trained the interviewers, coordinated field work, and participated in the drafting of this article. RL Henn contributed to study conception and design, the development of the data collection instrument, data analysis, and to the drafting of this article.

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