Tendências em fatores sociodemográficos e de estilo de vida associados ao comportamento sedentário em adultos brasileiros

Rev Bras Epidemiol rbepid Revista Brasileira de Epidemiologia Rev. bras. Epidemiol. 1415-790X 1980-5497 Associação Brasileira de Saúde Coletiva RESUMO: Objetivo: Analisar fatores sociodemográficos e de estilo de vida associados ao comportamento sedentário baseado em tempo de tela (assistir televisão ≥ 3 horas/dia) entre brasileiros adultos. Métodos: Análise quantitativa de dez edições do inquérito de saúde de delineamento transversal VIGITEL, representativo em nível populacional. Indivíduos de capitais estaduais residentes em domicílios com telefone fixo foram selecionados aleatoriamente e entrevistados via questionário estruturado por telefone. Estimou-se modelo de regressão logística multivariada para identificação de fatores associados ao comportamento sedentário. Resultados: Observou-se tendência estável na prevalência de comportamento sedentário entre 2008 e 2017. Verificou-se maior prevalência de comportamento sedentário entre indivíduos com padrões de comportamento menos saudáveis: consumo de < 2 itens alimentares in natura (vegetais, frutas e feijões) por dia (26,73% [IC95% 25,2 – 28,31]) em comparação ao consumo de ≥ 2 itens por dia (23,79% [IC95% 21,92 – 25,77]); consumo de refrigerantes em ≥ 5 dias por semana (31,24% [IC95% 29,58 – 32,95]) em comparação a < 5 dias por semana (23,82% [IC95% 22,2 – 25,52]); e prática de atividade física < 150 minutos por semana (28,2% [IC95% 26,17 – 30,33]) em comparação a ≥ 150 minutos por semana (22,54% [IC95% 21,27 – 23,86]). Consumir alimentos in natura (OR = 0,984); praticar atividade física (OR = 0,798) e residir em município de maior renda (OR = 0,826) representaram fatores de proteção ao comportamento sedentário baseado em tempo de tela, enquanto consumo de refrigerantes (OR = 1,440), fumo (OR = 1,375) e abuso de álcool (OR = 1,334) representaram fatores de risco. Conclusão: A adoção do comportamento sedentário baseado em tela entre indivíduos adultos no Brasil apresentou associação significativa com fatores comportamentais modificáveis no período 2008–2017. INTRODUCTION Sedentary behaviors represent significant risk factors for negative health outcomes. However, it differs substantially from lack of physical activity.1,2 Evidence on associations between sedentary behavior and chronic non-communicable diseases indicates significant association with cardiometabolic3 and cardiovascular4 diseases, cancer,5 overweight and obesity,6 and overall mortality.7 The recently published guidelines of the World Health Organization on physical activity and sedentary behavior recommend that adult individuals (18–64 years old) limit sedentarism, especially by replacing sedentary activities with physical activity at least 150 to 300 minutes per week due to substantial benefits to individuals’ health, which contributes to well-being and overall quality of life.8,9 Despite the harmful effects of sedentarism on health status, it presents high prevalence in diverse countries worldwide.8 A recent study indicated that approximately 65% of adults in the United States spent two or more hours watching television every day in 2015 and 2016.10 Sedentary behavior and physical activity may be performed in different domains, e.g., during leisure, transportation or labor, and other occupational or educational activities. In general, sedentary behaviors that occur during leisure are considered discretionary, and time spent watching television is usually adopted as proxy variable for optional sedentary behavior in epidemiological studies, especially considering its sensitivity to influences from cultural and socioeconomic contexts.11–13 Although there is emerging academic interest in factors associated with sedentary behaviors, most studies focus on high-income countries,12,13 and there is lack of evidence at population level for low- and middle-income countries, like Brazil, especially considering the simultaneity of health behaviors and health conditions during broad periods. Therefore, the objective of the present study was to analyze trends, and protective and risk factors associated with adoption of screen-based sedentary behavior (watching television ≥ three hours/day) in the adult population (≥ 18 years old) living in Brazilian state capitals from 2008 to 2017. METHODS STUDY DESIGN The study presents analysis of datasets from the Surveillance of Risk and Protection Factors for Prevention of Chronic Diseases through Telephone Survey (Vigilância de Fatores de Risco e Proteção para Doenças Crônicas por Inquérito Telefônico - VIGITEL), conducted by the Brazilian Ministry of Health, including ten years of cross-sectional observational individual-level data from representative sample of the adult population living in Brazilian state capitals and in the Federal District, from 2008 to 2017. DATABASES VIGITEL is a telephone survey on health, conducted annually since 2006 by the Brazilian Ministry of Health, to monitor risk and protection factors for chronic diseases in the Brazilian population. The databases include individual-level information for each year of the survey, available at the Brazilian Ministry of Health website. Microdata from surveys conducted from 2008 onwards were selected, considering the consolidation process of the survey during the first two years after its implementation. VIGITEL sampling process is based on the minimum sample of 1,500 individuals from each of the Brazilian state capitals and the Federal District to estimate the frequency of risk and protection factors for chronic diseases in the adult population with 95% confidence and maximum error of three percentage points.14 The first stage of sampling refers to random selection of at least 5,000 landlines per municipality from landline registrations of main telephone companies in the country. After initial drawing, lines eligible for survey are selected, only active residential lines. The second stage of sampling consists of randomly choosing one adult per household to participate in the survey.14 Considering the survey sample design, individuals interviewed are assigned weights to allow statistical inferences in relation to the population of 26 state capitals and the Brazilian Federal District, using rake method.14 Data collection was carried out with a structured interview by applying a closed questionnaire through the telephone.14 In addition to information from VIGITEL, data referring to Gross Domestic Product (GDP) and population of each municipality, obtained from the Brazilian Institute for Geography and Statistics (IBGE), were included in the dataset to represent certain environmental aspects of the municipality and population economic status, and to assess potential effects of economic conjuncture on other variables in the survey period (2008 to 2017). VARIABLES Sedentary behavior (outcome) was based on self-reported daily time watching television, considering sedentary individuals with screen time equal or higher than three hours per day. Variables of interest in the present study were: self-reported frequency of consumption of in natura food items (vegetables, fruits, and beans) per week;15 self-reported frequency of consumption of soft drinks per week;15 sociodemographic characteristics: age, biological sex, educational attainment, ethnicity/skin color, marital status, and occupation; health characteristics: self-assessment of health status, self-reported diagnosis of diabetes, self-reported diagnosis of hypertension, overweight, and obesity; self-reported behavioral characteristics: physical activity, alcohol abuse, and smoking; GDP per capita in the municipality in which individuals live, using data obtained from IBGE. DATA PROCESSING Information of VIGITEL databases from 2008 to 2017 were further organized into a single dataset, after selection of variables compatible throughout the analysis period to allow statistical analysis on trends and factors associated with adoption of screen-based sedentary behavior among adult individuals. A set of variables from VIGITEL was converted into binary variables, coded into zero (no) and one (yes) values, according to specific criteria based on evidence of the literature or cutoff points established by national and/or international organizations: screen-based sedentary behavior, and regular consumption of in natura food items and soft drinks. The adoption of screen-based sedentary behavior was based on self-report of daily time watching television, considering the cutoff point of three or more hours per day. Regular consumption of in natura food items and soft drinks was based on self-reported frequency per item: never, one to two days, three to four days, five to six days, or every day.14 Three variables registering self-reported frequency of in natura food items consumption, like beans, fruits, and vegetables (considered markers of healthier food consumption patterns), were converted into number of days per week consuming each item, which were added up and divided by seven days per week to comprise total in natura food items consumed per day. Then, cutoff point of at least two items per day during the week was used for categorization.14 Regular consumption of soft drinks (considered marker of unhealthier food consumption patterns) was categorized using cutoff point of consumption on five or more days during the week.15 Regarding sociodemographic characteristics, age and educational attainment were continuous variables maintained in their original format for analysis. Biological sex, ethnicity/skin color, marital status, and occupation were converted into categorical variables, encompassing the following categories, respectively: female (0) and male (1); white (0) and black, brown, and indigenous (1); living with companionship, i.e., marriage and stable union (0), and living with no companionship, i.e., being single, divorced, and widowed (1); and currently working (1) or not working (2). Amongst health characteristics, self-assessment of health status in five categories (very good, good, fair, poor, or very poor) was converted into a binary variable considering individuals who declared having poor or very poor health status. Presence of diabetes or hypertension were registered according to self-report of the individual. Occurrence of overweight (BMI ≥ 25 kg/m2)14 and obesity (BMI ≥ 30 kg/m2)14 in VIGITEL was based on the estimation of the Body Mass Index (BMI), based on self-reported information about weight and height.14 Behavioral characteristics were adopted in their original format from VIGITEL: physical activity level (≥ 150 minutes per week), alcohol abuse (≥ five doses for men; ≥ four doses for women at least on one occasion over the last 30 days), and smoking (current use of tobacco products, regardless of the amount).14 Values of GDP per capita were updated by applying the National Consumer Price Index (IPCA-IBGE), using the accumulated price index of the period of each annual survey to the reference period, January 2019. MODEL Multivariate logistic regression model was estimated to evaluate association of screen-based sedentary behavior with variables of interest selected, resulting in the identification of sociodemographic and lifestyle protection, and risk factors for adoption of screen-based sedentary behavior (outcome). The model included control variables for municipality, year of survey, and cross-effects of municipality and year of survey to capture potential influence of local policies. Analyzes were performed using the statistical software Stata® (Stata Corp., College Station, USA), version 14.2 for Windows, applying the svyset command for sample design, using rake weighting method, statistical significance p £ 0.05. ETHICAL ASPECTS The VIGITEL survey project was approved by the National Commission on Research Ethics (CAAE: 65610017.1.0000.0008). Informed consent was obtained verbally at the time of telephone contact.14 RESULTS Participants in the VIGITEL survey were usually female individuals, individuals who declared themselves black, brown, or indigenous, and individuals who worked. The proportion of young adults (18 to 39 years old) was higher during the first survey editions; nevertheless, there was an increasing trend in participation of older adults (40 to 59 years old), and elderly individuals (over 60 years old) (Table 1 and Supplementary Table 1). Table 1 Sociodemographic and health characteristics, and behaviors of participants, according to study year. Brazil, 2008–2017*. Characteristics Estimated prevalence, weighted p-value 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017 (n = 54,353) (n = 54,367) (n = 54,339) (n = 54,144) (n = 45,448) (n = 52,929) (n = 40,853) (n = 54,174) (n = 53,210) (n = 53,034) Sociodemographic characteristics Age (years) < 0.001 18–39 52.85 52.04 51.87 51.29 50.82 50.26 49.76 49.43 49.16 48.73 40–59 32.68 33.19 32.92 33.31 33.58 33.63 33.97 33.69 33.76 33.70 ≥ 60 14.47 14.77 15.21 15.40 15.60 16.11 16.27 16.88 17.08 17.57 Biological sex 0.199 Female 53.88 53.89 53.90 53.91 53.92 53.93 53.94 53.95 53.97 53.98 Male 46.12 46.11 46.10 46.09 46.08 46.07 46.06 46.05 46.03 46.02 Ethnicity/skin color < 0.001 White 39.03 39.24 39.97 43.50 40.57 41.47 39.75 40.80 43.60 42.04 Black to brown/indigenous 60.97 60.76 60.03 56.50 59.43 58.53 60.25 59.20 56.40 57.96 Marital status < 0.001 Married/stable union 50.19 51.29 51.61 49.31 50.96 48.84 50.31 47.66 47.79 46.33 Single/divorced/widowed 49.81 48.71 48.39 50.69 49.04 51.16 49.69 52.34 52.21 53.67 Educational level (years) < 0.001 0–8 43.63 41.97 40.57 38.07 36.77 36.51 35.94 34.58 32.49 30.80 9–11 34.72 35.85 35.88 36.08 38.54 37.58 38.12 38.11 35.87 37.28 ≥ 12 21.65 22.18 23.54 24.50 24.69 25.91 25.95 27.30 31.64 31.92 Occupation 0.014 Working 65.36 64.22 65.29 65.73 65.92 64.50 64.05 62.56 64.59 64.14 Not working 34.64 35.78 34.71 34.27 34.08 35.50 35.95 37.44 35.41 35.86 Health characteristics Overweight 44.88 45.98 48.19 48.82 51.01 50.77 52.52 53.92 53.82 54.00 < 0.001 Obesity 13.66 14.34 15.07 16.04 17.39 17.53 17.92 18.95 18.93 18.92 < 0.001 Diabetes diagnosis 6.22 6.34 6.78 6.29 7.37 6.87 8.04 7.40 8.94 7.63 < 0.001 Hypertension diagnosis 25.32 25.50 24.26 24.32 24.32 24.09 24.83 24.85 25.71 24.27 0.093 Poor health status 4.58 4.65 4.47 4.54 5.04 4.88 4.44 4.79 4.40 4.10 0.083 Health behaviors Screen-based sedentary behavior < 0.001 ≥ three hours a day in the week 24.60 24.04 27.25 25.93 26.41 28.58 25.31 22.53 25.70 24.60 < three hours a day in the week 75.40 75.96 72.75 74.07 73.59 71.42 74.69 77.47 74.30 75.40 Smoking 14.77 14.30 14.07 13.37 12.11 11.26 10.76 10.37 10.16 10.11 < 0.001 Alcohol abuse 17.22 18.44 18.08 16.52 18.43 16.38 16.51 17.18 19.09 19.06 < 0.001 In natura foods consumption < 0.001 < two groups per day 59.67 60.39 60.63 58.05 57.71 55.55 55.80 55.42 58.49 59.67 ≥ two groups per day 40.33 39.61 39.37 41.95 42.29 44.45 44.20 44.58 41.51 40.33 Soft drinks consumption < 0.001 ≥ five days a week 26.41 25.95 26.8 27.48 25.96 23.27 20.81 18.96 16.50 14.62 < five days a week 73.59 74.05 73.20 72.52 74.04 76.73 79.19 81.04 83.50 85.38 Physical activity practice <0.001 < 150 minutes per week 56.93 56.68 56.59 54.56 52.96 52.83 50.51 48.78 46.38 46.59 ≥ 150 minutes per week 43.07 43.32 43.41 45.44 47.04 47.17 49.49 51.22 53.62 53.41 * Data presented in number of individuals, n (%). p-values obtained from Pearson's chi-square test during the study years. The occurrence of individuals who self-reported certain health conditions increased throughout the period: diabetes (from 6.22 [95%CI 5.44 – 7.09] in 2008 to 7.63% [95%CI 7.10 – 8.19] in 2017; p < 0.001); obesity (from 13.66 [95%CI 13.18 – 14.15] in 2008 to 18.92% [95%CI 18.08 – 19.79] in 2017; p < 0.001); and overweight (from 44.88 [95%CI 43.64 – 46.14] in 2008 to 54.00% [95%CI 52.58 – 55.41] in 2017; p < 0.001) in the period (Table 1 and Supplementary Table 1). Adoption of screen-based sedentary behavior showed stability, presenting minor variations during the period (ranging from 22.53 [95%CI 21.11 – 24.02] in 2015 to 28.58% [95%CI 26.73 – 30.51] in 2013). However, the differences registered throughout the period were statistically significant (p < 0.001) (Table 1 and Supplementary Table 1). Among other behavioral characteristics, physical activity ≥ 150 minutes/week showed an increasing trend during the period (from 43.07 [95%CI 40.77 – 45.40] in 2008 to 53.41% [95%CI 49.57 – 57.21] in 2017; p < 0.001), as well as abusive alcohol consumption (from 17.22 [95%CI 14.75 – 20.01] in 2008 to 19.06% [95%CI 17.7 – 20.49] in 2017; p = 0.003). On the other hand, there was a decreasing trend in frequency of consumption of soft drinks (from 26.41 [95%CI 23.10 – 30.01] in 2008 to 14.62% [95%CI 11.71 – 18.10] in 2017; p < 0.001), and smoking (from 14.77 [12.73 – 17.07] in 2008 to 10.11% [7.94 – 12.79] in 2017; p < 0.001) (Table 1 and Supplementary Table 1). There were no statistically significant differences among individuals interviewed throughout the period regarding their biological sex, occupation, self-assessment of poor health status, and self-reported hypertension diagnosis (Table 1 and Supplementary Table 1). Results of the logistic model on the adoption of screen-based sedentary habits suggest statistically significant association with age, biological sex, ethnicity/skin color, marital status, educational attainment, and occupation: older individuals (OR = 0.999) and individuals with higher educational attainment (OR = 0.991) had lower probability to adopt screen-based sedentary behavior, whereas men (OR = 1.086), individuals who declared themselves black, brown, or indigenous (OR = 1.063), individuals living with no companionship (OR = 1.148), and individuals who were not working (OR = 1.889) had higher probability (Table 2). Table 2 Multivariate logistic model coefficients for screen-based sedentary behavior. Brazil, 2008–2017*. Watching TV ≥ three hours a day OR SE [95%CI] Sig. Age (years) 0.999 0.001 [0.998 – 1.000] ** Sex 1.086 0.016 [1.055 – 1.119] *** Ethnicity/skin color 1.063 0.016 [1.031 – 1.096] *** Marital status 1.148 0.017 [1.115 – 1.181] *** Educational level (years) 0.991 0.002 [0.988 – 0.995] *** Occupation 1.889 0.030 [1.832 – 1.948] *** Overweight 1.111 0.018 [1.076 – 1.146] *** Obesity 1.103 0.022 [1.061 – 1.147] *** Diabetes diagnosis 1.117 0.028 [1.064 – 1.174] *** Hypertension diagnosis 1.097 0.019 [1.059 – 1.135] *** Self-assessment of poor health 1.047 0.034 [0.982 – 1.116] ns In natura foods consumption 0.984 0.003 [0.979 – 0.990] *** Soft drinks consumption 1.440 0.026 [1.391 – 1.492] *** Physical activity practice 0.798 0.012 [0.775 – 0.822] *** Smoking 1.375 0.032 [1.315 – 1.438] *** Alcohol abuse 1.334 0.026 [1.283 – 1.386] *** Municipal GDP per capita (log) 0.826 0.007 [0.812 – 0.840] *** * Model estimated using sample weights, including control per municipality, survey year, and cross-effect of municipality and year ** p<0.05 *** p < 0.01; ns: not significant; OR: odds ratio; SE: robust standard errors; 95%CI: 95% confidence interval; Sig.: significance. There were also statistically relevant associations with overweight (OR = 1.111), obesity (OR = 1.103), self-reported diagnosis of diabetes (OR = 1.117), and hypertension (OR = 1.097). In relation to behavioral characteristics, results indicated that regular consumption of in natura foods (OR = 0.984) and practice of physical activity (OR = 0.798) were protective factors against the adoption of sedentary behavior, whereas the consumption of soft drinks (OR = 1.440), smoking (OR = 1.375), and alcohol abuse (OR = 1.334) were considered risk factors. Finally, considering the economic context, there was lower adherence to sedentary habits among individuals living in municipalities with higher per capita GDP (OR = 0.826) (Table 2). Self-assessment of poor health status (OR = 1.047) did not show statistical significance for screen-based sedentary behavior among adult individuals in the period analyzed. DISCUSSION The adoption of screen-based sedentary behavior (watching television ≥ three hours/day) among adult individuals in Brazil presented significant association with health behaviors that may be modifiable with public policies strategies designed for primary health care interventions. Evidence of the study emphasizes the role of sociodemographic, economic, and behavioral factors on lifestyle choices that influence the health status of the Brazilian population. Mechanisms of reinforcement between screen-based sedentary behavior and other behavior patterns were observed in previous studies. Healthier lifestyle choices, including frequent consumption of in natura foods15 and regular physical activity8 were protective factors against sedentary behavior of watching television ≥ three hours per day. Conversely, unhealthy behavior patterns were usually risk factors for sedentary behavior, including frequent consumption of soft drinks,15–17 smoking,18 and alcohol abuse.19 In Brazil, evidence referring to the protective role of regular in natura food consumption and recommended levels of physical activity in relation to the adoption of sedentary behavior was also observed in previous study performed among public school teachers in Presidente Prudente City, São Paulo State.20 In addition, there were also associations between overeating and alcohol consumption and time spent watching television.20 Results obtained in the study, reinforced by evidence in previous studies, draw attention to the concomitance and the repercussion of harmful habits to individual's health, indicating the importance of the discussion on the presence of multiple behavioral risk factors in relation to its impacts on health outcomes. Adherence to healthy lifestyles, with the combination of healthier behaviors, was significantly associated with reduction in premature death in the United States, resulting in increase in life expectancy, particularly healthy life years free from chronic non-communicable diseases (NCD).21,22 Similar evidence has been observed in studies with Brazilian adolescents23 and Polish adults,24 particularly regarding eating patterns associated with screen-based sedentary behavior (including watching television). It points out to underlying mechanisms of encouragement for consumption of food items that are considered markers of unhealthier food consumption patterns (e.g., soda, snacks, and sweets) while watching television. Furthermore, the habit of watching television has been related to body fat deposits,24 increasing risks of overweight, abdominal obesity, higher BMI, and waist circumference,6,25,26 which were partially also observed in the results of the present study. Overweight and obesity are important risk factors for NCD,27,28 being responsible for substantial health and economic burden in populations, health systems, and households worldwide, considering direct costs with treatments and indirect costs for individuals, such as productivity loss, compromised time of family members, and impacts on emotional health.29–31 Study results have shown that, besides overweight and obesity, individuals who self-reported diabetes and hypertension diagnosis were also more likely to adopt screen-based sedentary behavior, an association also observed in previous studies.3,32 Evidence on the relationship between NCD and sedentary behavior fosters the discussion on the need for engaging individuals diagnosed with NCD in initiatives that promote physical activity. Regarding perceived barriers, achieving recommended physical activity practice is especially important,33,34 as well as adopting healthier eating patterns,35,36 which highlight the social and environmental influences on behavioral change. In the context of sociodemographic characteristics, study results showed higher likelihood of screen-based sedentary behavior among individuals who declared being single, divorced, or widowed; that is, individuals who live with no companionship, in accordance with previous studies with Canadian and Japanese adults.37,38 However, a systematic review has shown certain inconsistencies regarding the influence of family and household factors, including marital status, on the adoption of sedentary behavior during leisure time.12 Therefore, although some evidence points to the adoption of screen-based sedentary behavior among individuals living unaccompanied, further research is required to identify whether marital status influences sedentary habits like watching television and its relationship with other sociodemographic factors over time. In any case, evidence calls for attention towards the discussions about the influence of one's companion to adopt healthier lifestyles, encouraging and/or accompanying the practice of physical activity during leisure, instead of sedentary recreational activities, like watching television.37,38 In terms of ethnicity/skin color, there is a higher likelihood in screen-based sedentary behaviors among individuals who declare themselves black, brown, or indigenous, which may be linked to environmental characteristics that impose barriers to physical activity practice in ethnic minorities, according to evidence from studies conducted in the United Kingdom39 and the United States.40 Thus, it represents an opportunity to discuss the design of health policy interventions with an equitable orientation, focusing on specific characteristics of the Brazilian black, brown, or indigenous individuals. Sedentary behavior presents socioeconomic and cultural determinants related to the organization of contemporary society, labor, and educational activities, i.e., routines that have been designed to occur generally in a sitting position, with minor energy expenditure, promoting sedentarism in individuals and populations.11 Whilst adherence to physical activity is commonly associated with leisure in high-income countries and work in low-income countries, both situations can be observed in middle-income countries like Brazil.41 Therefore, the adoption of indicators like watching television three or more hours per day for analyzing sedentary behavior may comprise an important marker of discretionary recreational activity, unlike other forms of screen-based sedentary behavior, like the duration of activities using computer, which may be linked to occupational activities. Our results showed that individuals who declared they were not working presented higher likelihood to maintain screen-based sedentary behavior during leisure by watching television ≥ three hours per day. However, considering differences observed in time spent in sedentary behaviors in diverse life domains in Brazil, assessed in a study conducted in Pelotas City, Rio Grande do Sul State,42 further investigating sedentary behavior in different life domains in the Brazilian population is of utmost importance. The main limitations of the present study refer to methodological characteristics of VIGITEL databases, especially its data collection, based on cross-sectional survey design,14 which impedes making causal relationships between screen-based sedentary behavior in relation to sociodemographic and behavioral characteristics of the Brazilian adult population. In addition, changes in the survey questionnaires throughout the analysis period limited the possibility of including certain characteristics of interest in the study, like the presence of hypercholesterolemia, consumption of other food items (milk, meat, and sweets), among others. Therefore, only variables that remained directly comparable during the period analyzed were selected in the study, allowing consistency for estimation of the model proposed. VIGITEL includes self-reported characteristics through telephone surveys, which may result in underestimation of characteristics that individuals believe are “wrong” or “socially unacceptable”, and an overestimation of characteristics perceived as “right” or “socially acceptable”, thus reducing the accuracy of analysis referring to certain individuals’ characteristics and behaviors. Furthermore, the variable for screen-based sedentary habit covers time spent watching television, and it does not include time spent with use of other devices, like computers, tablets, and mobile phones, which would potentially increase the prevalence of sedentariness in the Brazilian adult population, especially considering the widespread of information and communication technologies during the period analyzed. Sample selection in VIGITEL is based on population representativeness of individuals living in Brazilian state capitals and in the Federal District who have landline telephone, meaning areas of high urbanization.43 Thus, there is lack of representativeness of rural population in the study. Previous studies point to need to use alternative weighting strategies in the case of regions with low coverage of household landlines, pointing to potential underestimation biases due to the tendency to substitute the use of landlines by mobile phones throughout time.44,45 However, an assessment on the sampling and stratification processes adopted within VIGITEL indicated validity and representativeness for research, besides monitoring risk and protection factors related to the health status of the Brazilian population.46 Furthermore, sample size of the survey and its sampling procedures minimize potential biases in responses that potentially under- or overestimate monitoring of trends and risk or protection factors associated with sedentary behavior in the Brazilian adult population. Finally, increasing trends towards sedentarism, alcohol abuse, overweight, and obesity during the period analyzed represent a call for action within the context of the Brazilian health system, especially directed to primary health care strategies for health promotion and disease prevention. Considering the lack of cohort data representative at the national level in Brazil, study results may subsidize the formulation of strategic interventions in public health policies to promote healthy lifestyles among Brazilian adults. Financial support: Brazilian National Council for Scientific and Technological Development (CNPq). REFERENCES 1 Tremblay MS Colley RC Saunders TJ Healy GN Owen N Physiological and health implications of a sedentary lifestyle Appl Physiol Nutr Metab 2010 35 6 725 740 https://doi.org/10.1139/H10-079 1. Tremblay MS, Colley RC, Saunders TJ, Healy GN, Owen N. Physiological and health implications of a sedentary lifestyle. Appl Physiol Nutr Metab 2010; 35(6): 725-40. https://doi.org/10.1139/H10-079 2 Tremblay MS Aubert S Barnes JD Saunders TJ Carson V Latimer-Cheung AE Sedentary behavior research network (SBRN) - terminology consensus project process and outcome Int J Behav Nutr Phys Act 2017 14 75 75 https://doi.org/10.1186/s12966-017-0525-8 2. Tremblay MS, Aubert S, Barnes JD, Saunders TJ, Carson V, Latimer-Cheung AE, et al. Sedentary behavior research network (SBRN) - terminology consensus project process and outcome. Int J Behav Nutr Phys Act 2017; 14: 75. https://doi.org/10.1186/s12966-017-0525-8 3 Lim MS Park B Kong IG Sim S Kim SY Kim J Leisure sedentary time is differentially associated with hypertension, diabetes mellitus, and hyperlipidemia depending on occupation BMC Public Health 2017 17 278 278 https://doi.org/10.1186/s12889-017-4192-0 3. Lim MS, Park B, Kong IG, Sim S, Kim SY, Kim J, et al. Leisure sedentary time is differentially associated with hypertension, diabetes mellitus, and hyperlipidemia depending on occupation. BMC Public Health 2017; 17: 278. https://doi.org/10.1186/s12889-017-4192-0 4 Lavie CJ Ozemek C Carbone S Katzmarzyk PT Blair SN Sedentary behavior, exercise and cardiovascular health Circ Res 2019 124 5 799 815 https://doi.org/10.1161/CIRCRESAHA.118.312669 4. Lavie CJ, Ozemek C, Carbone S, Katzmarzyk PT, Blair SN. Sedentary behavior, exercise and cardiovascular health. Circ Res 2019; 124(5): 799-815. https://doi.org/10.1161/CIRCRESAHA.118.312669 5 Schimid D Leitzmann MF Television viewing and time spent sedentary in relation to cancer risk: a meta-analysis J Natl Cancer Inst 2014 106 7 dju098 https://doi.org/10.1093/jnci/dju098 5. Schimid D, Leitzmann MF. Television viewing and time spent sedentary in relation to cancer risk: a meta-analysis. J Natl Cancer Inst 2014; 106(7): dju098. https://doi.org/10.1093/jnci/dju098 6 Al-Hanawi MK Chirwa GC Pemba LA Qattan AMN Does prolonged television viewing affect Body Mass Index? A case of the Kingdom of Saudi Arabia PloS One 2020 15 1 e0228321 https://doi.org/10.1371/journal.pone.0228321 6. Al-Hanawi MK, Chirwa GC, Pemba LA, Qattan AMN. Does prolonged television viewing affect Body Mass Index? A case of the Kingdom of Saudi Arabia. PloS One 2020; 15(1): e0228321. https://doi.org/10.1371/journal.pone.0228321 7 Patterson R McNamara E Tainio M De Sá TH Smith AD Sharp SJ Sedentary behavior and risk of all-cause, cardiovascular and cancer mortality, and incident type 2 diabetes: a systematic review and dose response meta-analysis Eur J Epidemiol 2018 33 9 811 829 https://doi.org/10.1007/s10654-018-0380-1 7. Patterson R, McNamara E, Tainio M, De Sá TH, Smith AD, Sharp SJ, et al. Sedentary behavior and risk of all-cause, cardiovascular and cancer mortality, and incident type 2 diabetes: a systematic review and dose response meta-analysis. Eur J Epidemiol 2018; 33(9): 811-29. https://doi.org/10.1007/s10654-018-0380-1 8 World Health Organization Guidelines on physical activity and sedentary behaviour Geneva World Health Organization 2020 8. World Health Organization. Guidelines on physical activity and sedentary behaviour. Geneva: World Health Organization; 2020. 9 Dempsey PC Biddle SJH Buman MP Chastin S Ekelund U Friedenreich CM New global guidelines on sedentary behaviour and health for adults: broadening the behavioural targets Int J Behav Nutr Phys Act 2020 17 151 151 https://doi.org/10.1186/s12966-020-01044-0 9. Dempsey PC, Biddle SJH, Buman MP, Chastin S, Ekelund U, Friedenreich CM, et al. New global guidelines on sedentary behaviour and health for adults: broadening the behavioural targets. Int J Behav Nutr Phys Act 2020; 17: 151. https://doi.org/10.1186/s12966-020-01044-0 10 Yang L Cao C Kantor ED Nguyen LH Zheng X Park Y Trends in sedentary behavior among the US population, 2001-2016 JAMA 2019 321 16 1587 1597 https://doi.org/10.1001/jama.2019.3636 10. Yang L, Cao C, Kantor ED, Nguyen LH, Zheng X, Park Y, et al. Trends in sedentary behavior among the US population, 2001-2016. JAMA 2019; 321(16): 1587-97. https://doi.org/10.1001/jama.2019.3636 11 Bauman AE Petersen CB Blond K Rangul V Hardy LL The descriptive epidemiology of sedentary behaviour Leitzmann MF Jochem C Schmid D Sedentary behaviour epidemiology Cham Springer International 2018 73 106 11. Bauman AE, Petersen CB, Blond K, Rangul V, Hardy LL. The descriptive epidemiology of sedentary behaviour. In: Leitzmann MF, Jochem C, Schmid D, editors. Sedentary behaviour epidemiology. Cham: Springer International; 2018. p. 73-106. 12 O’Donoghue G Perchoux C Mensah K Lakerveld J Van der Ploeg H Bernaards C A systematic review of correlates of sedentary behaviour in adults aged 18-65 years: a socio-ecological approach BMC Public Health 2016 16 163 163 https://doi.org/10.1186/s12889-016-2841-3 12. O’Donoghue G, Perchoux C, Mensah K, Lakerveld J, Van der Ploeg H, Bernaards C, et al. A systematic review of correlates of sedentary behaviour in adults aged 18-65 years: a socio-ecological approach. BMC Public Health 2016; 16: 163. https://doi.org/10.1186/s12889-016-2841-3 13 Prince SA Reed JL McFetridge C Tremblay MS Reid RD Correlates of sedentary behaviour in adults: a systematic review Obes Rev 2017 18 8 915 935 https://doi.org/10.1111/obr.12529 13. Prince SA, Reed JL, McFetridge C, Tremblay MS, Reid RD. Correlates of sedentary behaviour in adults: a systematic review. Obes Rev 2017; 18(8): 915-35. https://doi.org/10.1111/obr.12529 14 Brasil. Ministério da Saúde VIGITEL Brasil 2017: vigilância de fatores de risco e proteção para doenças crônicas por inquérito telefônico: estimativas sobre frequência e distribuição sociodemográfica de fatores de risco e proteção para doenças crônicas nas capitais dos 26 estados brasileiros e no Distrito Federal em 2017 Brasília Ministério da Saúde 2018 14. Brasil. Ministério da Saúde. VIGITEL Brasil 2017: vigilância de fatores de risco e proteção para doenças crônicas por inquérito telefônico: estimativas sobre frequência e distribuição sociodemográfica de fatores de risco e proteção para doenças crônicas nas capitais dos 26 estados brasileiros e no Distrito Federal em 2017. Brasília: Ministério da Saúde; 2018. 15 Brasil. Ministério da Saúde Guia alimentar para a população brasileira Brasília Ministério da Saúde 2014 15. Brasil. Ministério da Saúde. Guia alimentar para a população brasileira. Brasília: Ministério da Saúde; 2014. 16 Mulee A Romaguera D Pearson-Stuttard J Viallon V Stepien M Freisling H Association between soft drink consumption and mortality in 10 European countries Jama Intern Med 2019 179 11 1479 1490 https://doi.org/10.1001/jamainternmed.2019.2478 16. Mulee A, Romaguera D, Pearson-Stuttard J, Viallon V, Stepien M, Freisling H, et al. Association between soft drink consumption and mortality in 10 European countries. Jama Intern Med 2019; 179(11): 1479-90. https://doi.org/10.1001/jamainternmed.2019.2478 17 Garduño-Alanís A Malyutina S Pajak A Stepaniak U Kunivoa R Denisova R Association between soft drink, fruit juice consumption and obesity in Eastern Europe: cross-sectional and longitudinal analysis of the HAPIEE study J Hum Nutr Diet 2020 33 1 66 77 https://doi.org/10.1111/jhn.12696 17. Garduño-Alanís A, Malyutina S, Pajak A, Stepaniak U, Kunivoa R, Denisova R, et al. Association between soft drink, fruit juice consumption and obesity in Eastern Europe: cross-sectional and longitudinal analysis of the HAPIEE study. J Hum Nutr Diet 2020; 33(1): 66-77. https://doi.org/10.1111/jhn.12696 18 West R Tobacco smoking: health impact, prevalence, correlates and interventions Psychol Health 2017 32 8 1018 1036 https://doi.org/10.1080/08870446.2017.1325890 18. West R. Tobacco smoking: health impact, prevalence, correlates and interventions. Psychol Health 2017; 32(8): 1018-36. https://doi.org/10.1080/08870446.2017.1325890 19 Whitman IR Agarwal V Nah G Dukes JW Vittinghoff E Dewland TA Alcohol abuse and cardiac disease J Am Coll Cardiol 2017 69 1 13 24 https://doi.org/10.1016/j.jacc.2016.10.048 19. Whitman IR, Agarwal V, Nah G, Dukes JW, Vittinghoff E, Dewland TA, et al. Alcohol abuse and cardiac disease. J Am Coll Cardiol 2017; 69(1): 13-24. https://doi.org/10.1016/j.jacc.2016.10.048 20 Delfino LD Tebar WR Gil FC De Souza JM Romanzini M Fernandes RA Association of sedentary behaviour patterns with dietary and lifestyle habits among public school teachers: a cross-sectional study BMJ Open 2020 10 e034322 https://doi.org/10.1136/bmjopen-2019-034322 20. Delfino LD, Tebar WR, Gil FC, De Souza JM, Romanzini M, Fernandes RA, et al. Association of sedentary behaviour patterns with dietary and lifestyle habits among public school teachers: a cross-sectional study. BMJ Open 2020; 10: e034322. https://doi.org/10.1136/bmjopen-2019-034322 21 Li Y Schoufour J Wang DD Dhana K Pan A Liu X Healthy lifestyle and life expectancy free of cancer, cardiovascular disease, and type 2 diabetes: prospective cohort study BMJ 2020 368 I6669 https://doi.org/10.1136/bmj.l6669 21. Li Y, Schoufour J, Wang DD, Dhana K, Pan A, Liu X, et al. Healthy lifestyle and life expectancy free of cancer, cardiovascular disease, and type 2 diabetes: prospective cohort study. BMJ 2020; 368: I6669. https://doi.org/10.1136/bmj.l6669 22 Li Y Pan A Wang DD Liu X Dhana K Franco OH The impact of healthy lifestyle factors on life expectancies in the US population Circulation 2018 138 4 345 355 https://doi.org/10.1161/CIRCULATIONAHA.117.032047 22. Li Y, Pan A, Wang DD, Liu X, Dhana K, Franco OH, et al. The impact of healthy lifestyle factors on life expectancies in the US population. Circulation 2018; 138(4): 345-55. https://doi.org/10.1161/CIRCULATIONAHA.117.032047 23 Haddad MR Sarti FM Sociodemographic determinants of health behaviors among Brazilian adolescents: trends in physical activity and food consumption, 2009-2015 Appetite 2020 144 104454 https://doi.org/10.1016/j.appet.2019.104454 23. Haddad MR, Sarti FM. Sociodemographic determinants of health behaviors among Brazilian adolescents: trends in physical activity and food consumption, 2009-2015. Appetite 2020; 144: 104454. https://doi.org/10.1016/j.appet.2019.104454 24 Jezewska-Zychowicz M Gębski J Guzek D Świątkowska M Stangierska D Plichta M The associations between dietary patterns and sedentary behaviors in polish adults (LifeStyle Study) Nutrients 2018 10 8 1004 1004 https://doi.org/10.3390/nu10081004 24. Jezewska-Zychowicz M, Gębski J, Guzek D, Świątkowska M, Stangierska D, Plichta M, et al. The associations between dietary patterns and sedentary behaviors in polish adults (LifeStyle Study). Nutrients 2018; 10(8): 1004. https://doi.org/10.3390/nu10081004 25 Wang Y Su C Ouyang Y Jia X Zhang B Wang Z Secular trends in sedentary behaviors and associations with weight indicators among Chinese reproductive-age women from 2004 to 2015: findings from the China Health and Nutrition Survey Int J Obes 2020 44 2267 2278 https://doi.org/10.1038/s41366-020-00684-3 25. Wang Y, Su C, Ouyang Y, Jia X, Zhang B, Wang Z, et al. Secular trends in sedentary behaviors and associations with weight indicators among Chinese reproductive-age women from 2004 to 2015: findings from the China Health and Nutrition Survey. Int J Obes 2020; 44: 2267-78. https://doi.org/10.1038/s41366-020-00684-3 26 Gupta RD Haider SS Hashan MR Hasan M Sutradhar I Sajal IH Association between the frequency of television watching and overweight and obesity among women of reproductive age in Nepal: analysis of data from the Nepal Demographic and Health Survey 2016 PLoS One 2020 15 2 e0228862 https://doi.org/10.1371/journal.pone.0228862 26. Gupta RD, Haider SS, Hashan MR, Hasan M, Sutradhar I, Sajal IH, et al. Association between the frequency of television watching and overweight and obesity among women of reproductive age in Nepal: analysis of data from the Nepal Demographic and Health Survey 2016. PLoS One 2020; 15(2): e0228862. https://doi.org/10.1371/journal.pone.0228862 27 Nowbar AN Gitto M Howard JP Francis DP Al-Lamee R Mortality from ischemic heart disease Circ Cardiovasc Qual Outcomes 2019 12 6 e005375 https://doi.org/10.1161/CIRCOUTCOMES.118.005375 27. Nowbar AN, Gitto M, Howard JP, Francis DP, Al-Lamee R. Mortality from ischemic heart disease. Circ Cardiovasc Qual Outcomes 2019; 12(6): e005375. https://doi.org/10.1161/CIRCOUTCOMES.118.005375 28 World Health Organization Global status report on noncommunicable diseases 2014 Geneva World Health Organization 2014 28. World Health Organization. Global status report on noncommunicable diseases 2014. Geneva: World Health Organization; 2014. 29 Lehnert T Sonntag D Konnopka A Riedel-Heller S König H Economic costs of overweight and obesity Best Pract Res Clin Endocrinol Metab 2013 27 2 105 115 https://doi.org/10.1016/j.beem.2013.01.002 29. Lehnert T, Sonntag D, Konnopka A, Riedel-Heller S, König H. Economic costs of overweight and obesity. Best Pract Res Clin Endocrinol Metab 2013; 27(2): 105-15. https://doi.org/10.1016/j.beem.2013.01.002 30 Jan S Laba T Essue BM Gheorghe A Muhunthan J Engelgau M Action to address the household economic burden of non-communicable diseases Lancet 2018 391 10134 2047 2058 https://doi.org/10.1016/S0140-6736(18)30323-4 30. Jan S, Laba T, Essue BM, Gheorghe A, Muhunthan J, Engelgau M, et al. Action to address the household economic burden of non-communicable diseases. Lancet 2018; 391(10134): 2047-58. https://doi.org/10.1016/S0140-6736(18)30323-4 31 Bertram MY Sweeny K Lauer J Chisholm D Sheehan P Rasmussen B Investing in non-communicable diseases: an estimation of the return on investment for prevention and treatment services Lancet 2018 391 10134 2071 2078 https://doi.org/10.1016/S0140-6736(18)30665-2 31. Bertram MY, Sweeny K, Lauer J, Chisholm D, Sheehan P, Rasmussen B. Investing in non-communicable diseases: an estimation of the return on investment for prevention and treatment services. Lancet 2018; 391(10134): 2071-8. https://doi.org/10.1016/S0140-6736(18)30665-2 32 Gao Y Xie X Wang S Li H Tang H Zhang J Effects of sedentary occupations on type 2 diabetes and hypertension in different ethnic groups in North West China Diab Vasc Dis Res 2017 14 4 372 375 https://doi.org/10.1177%2F1479164117696050 32. Gao Y, Xie X, Wang S, Li H, Tang H, Zhang J, et al. Effects of sedentary occupations on type 2 diabetes and hypertension in different ethnic groups in North West China. Diab Vasc Dis Res 2017; 14(4): 372-5. https://doi.org/10.1177%2F1479164117696050 33 Chang C Khurana S Strodel R Camp A Magenheimer E Hawley N Perceived barriers to physical activity among low-income Latina women at risk for type 2 diabetes Diabetes Educ 2018 44 5 444 453 https://doi.org/10.1177%2F0145721718787782 33. Chang C, Khurana S, Strodel R, Camp A, Magenheimer E, Hawley N. Perceived barriers to physical activity among low-income Latina women at risk for type 2 diabetes. Diabetes Educ 2018; 44(5): 444-53. https://doi.org/10.1177%2F0145721718787782 34 Gee ME Bienek A Campbell NRC Bancej CM Robitaille C Kaczorowski J Prevalence of, and barriers to, preventive lifestyle behaviors in hypertension (from a national survey of Canadians with hypertension) Am J Cardio 2012 109 4 570 575 https://doi.org/10.1016/j.amjcard.2011.09.051 34. Gee ME, Bienek A, Campbell NRC, Bancej CM, Robitaille C, Kaczorowski J, et al. Prevalence of, and barriers to, preventive lifestyle behaviors in hypertension (from a national survey of Canadians with hypertension). Am J Cardio 2012; 109(4): 570-5. https://doi.org/10.1016/j.amjcard.2011.09.051 35 Halali F Mahdavi R Mobasseri M Jafarabadi MA Avval SK Perceived barriers to recommended dietary adherence in patients with type 2 diabetes in Iran Eat Behav 2016 21 205 210 https://doi.org/10.1016/j.eatbeh.2016.03.001 35. Halali F, Mahdavi R, Mobasseri M, Jafarabadi MA, Avval SK. Perceived barriers to recommended dietary adherence in patients with type 2 diabetes in Iran. Eat Behav 2016; 21: 205-10. https://doi.org/10.1016/j.eatbeh.2016.03.001 36 Mahdavi R Bagheri A Abadi MAJ Namazi N Perceived barriers to following dietary recommendations in hypertensive patients J Am Coll Nutr 2017 36 3 193 199 https://doi.org/10.1080/07315724.2014.966176 36. Mahdavi R, Bagheri A, Abadi MAJ, Namazi N. Perceived barriers to following dietary recommendations in hypertensive patients. J Am Coll Nutr 2017; 36(3): 193-9. https://doi.org/10.1080/07315724.2014.966176 37 Huffman S Szafron M Social correlates of leisure-time sedentary behaviours in Canadian adults Prev Med Rep 2017 5 268 274 https://doi.org/10.1016/j.pmedr.2017.01.007 37. Huffman S, Szafron M. Social correlates of leisure-time sedentary behaviours in Canadian adults. Prev Med Rep 2017; 5: 268-74. https://doi.org/10.1016/j.pmedr.2017.01.007 38 Ishii K Shibata A Oka K Sociodemographic and anthropometric factors associated with screen-based sedentary behavior among Japanese adults: a population-based cross-sectional study J Epidemiol 2013 23 5 382 388 https://doi.org/10.2188/jea.JE20130008 38. Ishii K, Shibata A, Oka K. Sociodemographic and anthropometric factors associated with screen-based sedentary behavior among Japanese adults: a population-based cross-sectional study. J Epidemiol 2013; 23(5): 382-8. https://doi.org/10.2188/jea.JE20130008 39 Ige-Elegbede J Pilkington P Gray S Powell J Barriers and facilitators of physical activity among adults and older adults from black and minority ethnic groups in the UK: a systematic review of qualitative studies Prev Med Rep 2019 15 100952 https://doi.org/10.1016/j.pmedr.2019.100952 39. Ige-Elegbede J, Pilkington P, Gray S, Powell J. Barriers and facilitators of physical activity among adults and older adults from black and minority ethnic groups in the UK: a systematic review of qualitative studies. Prev Med Rep 2019; 15: 100952. https://doi.org/10.1016/j.pmedr.2019.100952 40 Hughey SM Walsemann KM Child S Powers A Reed JA Kaczynski AT Using an environmental justice approach to examine the relationships between park availability and quality indicators, neighborhood disadvantage, and racial/ethnic composition Landsc Urban Plan 2016 148 159 169 https://doi.org/10.1016/j.landurbplan.2015.12.016 40. Hughey SM, Walsemann KM, Child S, Powers A, Reed JA, Kaczynski AT. Using an environmental justice approach to examine the relationships between park availability and quality indicators, neighborhood disadvantage, and racial/ethnic composition. Landsc Urban Plan 2016; 148: 159-69. https://doi.org/10.1016/j.landurbplan.2015.12.016 41 O’Donoghue G Kennedy A Puggina A Aleksovska K Buck C Burns C Socio-economic determinants of physical activity across the life course: A “DEterminants of DIet and Physical ACtivity” (DEDIPAC) umbrella literature review PLoS One 2018 13 1 e0190737 https://doi.org/10.1371/journal.pone.0190737 41. O’Donoghue G, Kennedy A, Puggina A, Aleksovska K, Buck C, Burns C, et al. Socio-economic determinants of physical activity across the life course: A “DEterminants of DIet and Physical ACtivity” (DEDIPAC) umbrella literature review. PLoS One 2018; 13(1): e0190737. https://doi.org/10.1371/journal.pone.0190737 42 Mielke GI Da Silva ICM Owen N Hallal PC Brazilian adults’ sedentary behaviors by life domain: population-based study PLoS One 2014 9 3 e91614 https://doi.org/10.1371/journal.pone.0091614 42. Mielke GI, Da Silva ICM, Owen N, Hallal PC. Brazilian adults’ sedentary behaviors by life domain: population-based study. PLoS One 2014; 9(3): e91614. https://doi.org/10.1371/journal.pone.0091614 43 Instituto Brasileiro de Geografia e Estatística Áreas urbanizadas do Brasil Rio de Janeiro IBGE 2015 43. Instituto Brasileiro de Geografia e Estatística. Áreas urbanizadas do Brasil. Rio de Janeiro: IBGE; 2015. 44 Bernal RTI Malta DC Claro RM Monteiro CA Efeito da inclusão de entrevistas por telefone celular ao VIGITEL Rev Saúde Pública 2017 51 Suppl. 1 1s 5s https://doi.org/10.1590/s1518-8787.2017051000171 44. Bernal RTI, Malta DC, Claro RM, Monteiro CA. Efeito da inclusão de entrevistas por telefone celular ao VIGITEL. Rev Saúde Pública 2017; 51(Suppl. 1): 1s-5s. https://doi.org/10.1590/s1518-8787.2017051000171 45 Bernal RTI Malta DC De Morais OL Neto Claro RM Mendoça BCA Oliveira ACC VIGITEL-Aracaju, Sergipe, 2008: efeitos da pós-estratificação na correção de vícios decorrentes da baixa cobertura de domicílios com telefone fixo Rev Bras Epidemiol 2014 17 1 163 174 https://doi.org/10.1590/1415-790X201400010013ENG 45. Bernal RTI, Malta DC, De Morais Neto OL, Claro RM, Mendoça BCA, Oliveira ACC, et al. VIGITEL-Aracaju, Sergipe, 2008: efeitos da pós-estratificação na correção de vícios decorrentes da baixa cobertura de domicílios com telefone fixo. Rev Bras Epidemiol 2014; 17(1): 163-74. https://doi.org/10.1590/1415-790X201400010013ENG 46 Bernal RTI Iser BPM Malta DC Claro RM Surveillance System for Risk and Protective Factors for Chronic Diseases by Telephone Survey (VIGITEL): changes in weighting methodology Epidemiol Serv Saúde 2017 26 4 701 712 https://doi.org/10.5123/s1679-49742017000400003 46. Bernal RTI, Iser BPM, Malta DC, Claro RM. Surveillance System for Risk and Protective Factors for Chronic Diseases by Telephone Survey (VIGITEL): changes in weighting methodology. Epidemiol Serv Saúde 2017; 26(4): 701-12. https://doi.org/10.5123/s1679-49742017000400003