Co-occurrence of Leisure Screen Time and Leisure-time Physical Activity Among Brazilians and Associated Socioeconomic, Demographic, and Health Factors

Co-occurrence of Leisure Screen Time and Leisure-time Physical Activity Among Brazilians and Associated Socioeconomic, Demographic, and Health Factors | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Short Report Co-occurrence of Leisure Screen Time and Leisure-time Physical Activity Among Brazilians and Associated Socioeconomic, Demographic, and Health Factors Pollyanna Costa Cardoso, Taciana Maia de SOUSA, Marcela Mello SOARES, and 3 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8139253/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Background This study analyzed the co-occurrence of leisure screen time and leisure-time physical activity in Brazilian adults, and their association with socioeconomic and demographic characteristics, nutritional status, and chronic health conditions. Methods Data from individuals aged ≥ 18 years from the 2019 National Health Survey (n = 88,531). Cluster analysis identifield the co-occurrence of screen use (TV and cell phone, computer, or tablet [CCT]) during leisure time and physical activity (leisure, commuting, home, and work). Multivariate multinomial logistic regression was used to identify the factors associated with the clusters. Results Four clusters were identified: (1) low screen use during leisure time and active individuals, especially at work (13.0% of the total population); (2) prolonged TV use and predominance inactive individuals (14.9%); (3) prolonged CCT use and active individuals, especially during leisure time (18.0%); (4) low screens use during leisure time and active individuals (54.1%). Compared to Cluster 1, Cluster 2 was more likely among women (OR: 2.78; p < .001), individuals ≥ 60 years old (OR: 8.41; p < .001), with obesity (OR: 1.32; p < .05), diabetes, arterial hypertension, and other cardiovascular diseases (OR: 1.78; 1.27; 1.52, respectively; p < .001) were more associated with cluster 2, while those with higher education (OR: 3.22; p < .001) and higher income (OR: 2.11; p < .001) were more associated with cluster 3, as were those aged < 35 years. Conclusion Four lifestyle clusters with socioeconomic, demographic, and health differences were identified, offering relevant support for the development of public health policies. Cluster screen time physical exercise behavior patterns health inequities public health INTRODUCTION Reducing sedentary behavior and increasing physical activity have been global priorities on the Public Health agenda 1 – 3 . Prolonged time spent in sedentary activities, especially watching television (TV), is associated with the increased risk of adverse health outcomes, related to Noncommunicable Diseases (NCDs) 1 , 4 , 5 , to mental health 6 , and to all-cause mortality 4 , 5 , 7 . Evidence shows that risks are potentially higher when prolonged sedentary behavior and a lack of physical activity happen simultaneously 5 , 8 . Despite this, the prevalence of sedentary behaviors has been increasing 4 and the prevalence of physical activity still remains high 3 , 9 . Estimates from the last decade showed that one in every four adults, in high income countries 10 , as well as in countries like Brazil 11 , 12 , reported watching TV for three hours or more per day. Adding other screens to this figure – such as cell phone, computer, or tablet (CCT) for leisure purposes – increases this frequency considerably. Among Brazilian adults residing in state capitals and the Federal District, 62.7% reported spending three hours or more per day watching TV or using CCT for leisure in 2019 12 . Studies have shown a decrease in the habit of watching TV, as compared to an increase in the prevalence of the prolonged use of CCT for leisure among Brazilians over time (2008/19) 15–16 . In parallel, the frequency of adults who do not meet the current recommendation of physical activities from the World Health Organization (WHO) (150–300 minutes/week of aerobic activity of moderate intensity, or the equivalent in vigorous activity 4 ) was 27.5% in 2016, globally 9 , and 40.3% in 2019, in Brazil (considering leisure, commuting, and work domains) 11 . Although the prevalence of physical activities in leisure time and in total (especially by the contribution of leisure-time physical activities, as well as by physical activities at work) increased among Brazilians from 2003 to 2019, an increase in social inequities was revealed as well 15 – 16 . Studies with populations from low- and middle-income countries have mainly investigated one or more of these behaviors in an isolated manner, such as the amount of time sitting down, the amount of time in front of a screen, total physical activity, and/or by domains, and its associations with sociodemographic factors 13 – 16 . Few studies have investigated the co-occurrence of these behaviors and associated factors. Behavior patterns which are potentially prejudicial to health (particularly related to time sitting down and to total physical activity and/or by domains) were identified in high-income countries, given that sex, age, and socioeconomic status were important determinants 17 , 18 . Studies of this nature in Brazil are scarce, and the findings were limited to a non-probabilistic sub-sample 19 (elderly) or to atypical periods (compulsory confinement during the COVID-19 pandemic), with methodological differences 20 . The lack of evidence regarding behavior patterns related to different types/domains of sedentary behavior and physical activities, as well as associated factors, reveals gaps that need to be filled 21 . The identification of clusters of these behaviors in a representative sample of the population of a middle-income country, as well as the socioeconomic, demographic, and health factors, which differ among lifestyles that are somewhat healthy, allows for a better understanding and helps to develop and prioritize more efficient interventions, Therefore, the objective of this study is to analyze the co-occurrence of the use of screens and physical activities in the adult Brazilian population, together with its association with socioeconomic and demographic characteristics, nutritional status, and chronic health conditions. METHODOLOGY Study design This is a cross-sectional study conducted with data from the 2019 National Health Survey ( Pesquisa Nacional de Saúde - PNS), a household survey representative of the Brazilian population conducted by the Ministry of Health and the Brazilian Institute of Geography and Statistics, every five years 11 . Sampling and data collection The 2019 PNS uses cluster sampling in three stages: in the first, all of the Primary Sampling Units (PSU) were selected, made up of census sectors or groups of sectors. In the second stage, a particular permanent household was chosen from each PSU. Finally, in the third stage, a resident, aged 15 years or older, was randomly chosen among the eligible members of that household 11 , 22 . The interviews were conducted between August 2019 and March 2020, by means of mobile devices (smartphones) for the collection of information regarding health determinants and conditioning factors of the population 11 , 22 . The sample included 8,036 PSUs, 108,525 households, and 90,846 residents, aged 15 years or older, with residences throughout Brazil 11 . For the current study, a sub-sample was used, comprised exclusively of residents, aged 18 years or older, totalling 88,531 adults. The data from the PNS was weighted by means of sampling weights, for both households and selected residents. These weights were calculated based on the inverse of the product of the selection probabilities in each of the three sampling stages, incorporating a correction factor for losses and making adjustments to the population totals 11 , 22 . More details about the methodological procedures, questionnaire, and database of the 2019 PNS are available in the electronic site < https://www.pns.icict.fiocruz.br/%3E . Variables and indicators The indicators of sedentary behavior (TV and CCT time) during leisure time and during the practice of physical activities in the four domains (leisure, commuting, home, and work) included in this study are described in Chart 1 . Additionally, derived indicators were considered: the sum of TV and CCT times, the sum of physical activity in the domains of leisure and commutting, and the total amount of physical activity in the four domains together. The socioeconomic and demographic, nutritional status, and chronic health condition variables, which complemented the analyses, were: sex (male | female); age group in years (18–34 | 35–59 | ≥ 60); education in years of schooling (0–8 | 9–11 | ≥ 12); per capita income in Minimum Wage units (MW) (*< 1 MW (from R $ 0 to R $ 997 reais) | ≥ 1 MW to < 3 MW (R $ 998 to R $ 2993) | ≥ 3 to < 5 MW (R $ 2994 to R $ 4989) | ≥ 5 MW (R $ 4990)), Body Mass Index (BMI) (< 18.5: low weight | ≥ 18.5 and < 25 kg/m 2 : eutrophic | ≥ 25 and < 30 kg/m 2 : pre-obesity | ≥ 30 kg/m 2 : obesity 24 ); and chronic health conditions (diabetes | arterial hypertension | other cardiovascular diseases, as well as stroke, acute myocardial infarction, angina, and heart failure). Data analysis Measures of central tendency and dispersion of indicators of screen time and physical activity time were estimated for the entire population and stratified by sex. Cluster analysis was employed for the identification of co-occurrence (or clustering) of screen time and physical activities by the use of the K-means non-hierarchical method. The cluster analysis was conducted with six indicators: TV and CCT time during leisure, and time of physical activities in the four domains: leisure, commuting, home, and work, for the total population and by sex. The unit of time spent on each type of behavior was converted into minutes/week for the cluster analysis, and was later presented in its original unit to facilitate the interpretation of the results. Since cluster analyses are sensitive to outliers, these were identified in the physical activity variables for the four domains (corresponding to approximately 15% of the total population). These observations had their values replaced by the mean value plus two standard deviations (adopted as maximum value - cutoff point) for each indicator. The choice of the number of clusters for the total population and by sex was based on the Calinski/Harabasz pseudo-F evaluation criteria between analyses with 2, 3, 4, 5, and 6 solutions. The reliability and stability of the number of final solutions were tested in repeated analyses of three random subsamples (equivalent to 50%) stemming from the total sample (of the total population and for each sex). The cluster analysis is considered satisfactory when the results obtained for the subsamples are similar to what is found for the total sample 18 . The Kappa coefficient was calculated to analyze the agreement of the final cluster solution of each subsample with that of the total sample, and indicated excellent agreement (Kappa > 0.99). The Levene test was conducted to evaluate the homogeneity of variances of the indicators of leisure screen time and leisure-time physical activity of the clusters. Next, the ANOVA and post hoc Bonferroni tests were conducted to reveal differences in the average time of these indicators between the clusters, for the total population and by sex. To characterize the clusters, the frequency of the indicator of each behavior was also used (≥ 3 h/day considered prolonged use of screen | < 3 h/day considered low use of screen 25 , and ≥ 150 min/week of aerobic physical activity of moderate intensity (or the equivalent in vigorous activity) was considered to be active | < 150 min/week was considered to be insufficiently active 4 ). Moreover, frequencies were estimated for new indicators, which included the sum of TV and CCT time (total screen time), the time spent on leisure-time physical activity and commuting (probable discretionary domains), and the time of physical activity in all of the domains 4 (total time of physical activity). Multivariate multinomial logistic regression models were used to verify the associations between outcome variables (clusters) and explanatory variables (socioeconomic and demographic characteristics, nutritional status, and chronic health conditions). Initially, simple multinomial logistic regression models were constructed with each explanatory variable. Next, all variables that presented a p-value ≤ 0.20 in the simple regression models were taken to the multivariate model, and their adjustment was performed using the backward method (p < 0.05). The distribution (%) and the adjusted Odds Ratio (OR) for all of the explanatory variables included in this study were estimated. All of the analyses were conducted for the total population and stratified by sex. The analyses were conducted with the Stata version 16.1 app (StataCorp LP, College Station, USA), using the survey module for complex samples, which incorporates the weighting factors (except in the partition of groups in the cluster analysis). Ethical aspects The 2019 PNS was approved by the National Research Ethics Committee of the National Health Council, from the Ministry of Health (protocol no. 3,529,376). A free and informed consent form was previously signed by the participants. RESULTS In the total population, average TV time (2.2 h/day) was similar to CCT time during leisure (1.9 h/day). The practice of physical activities varied between the domains, with the highest average registered at work (296.2 min/week), followed by activities during leisure time (113.3 min/week), while commuting (84.7 min/week), and at home (42.6 min/week)). Compared to women, men spent more time in activities during leisure (133.6 vs. 95.4 min/week) and at work (418.2 vs. 188.7 min/week), and less time watching TV (2.2 vs. 2.3 h/day) and less involvement in activities at home (20.1 vs. 62.4 min/week) (Table 1). Four distinct clusters of leisure screen time and leisure-time physical activities were identified in the total population and by sex. The comparison of the averages of the indicators between the clusters showed that individuals in cluster 1 (13.0% of the population) had the highest average times of physical activity while commuting (116.1 min/week), at home (51.0 min/week), and at work (1593.9 min/week). In cluster 2 (14,9%), the highest average time of exposure to TV (4.9 h/day) was observed, and individuals practiced less activities during leisure (83.7 min/week) and at home (37.7 min/week). Meanwhile, those in cluster 3 (18.0%) used more CCT during leisure time (5.1 h/day) and practiced more leisure-time physical activities (156.6 min/week). Finally, those in cluster 4 (54.1%) used the screen less during leisure time (1.5 h/day of TV and 1.1 h/day of CCT) and practiced more physical activities at work (58.6 min/week). The analysis stratified by sex revealed differences in the population distribution of the clusters (men had a higher representation in clusters 1 and 2 and lower in cluster 4, when compared to women) and especially, in averages of TV time (in cluster 2, women watched more TV than men) and of physical activity during leisure time and at work (in all the clusters, men practiced more activities than women), and at home (in all the clusters, women practiced more activities than men) (Table 2). The cluster classification analysis revealed that cluster 1 was characterized by a low leisure screen time (around 90% of the individuals used TV or CCT < 3h/day) and a high prevalence of active individuals (100% practiced ≥ 150 min/week of total physical activity, especially at the work domain). Cluster 2 showed a prolonged use of TV (100% watched TV ≥ 3 h/day), absence of prolonged exposure to CCT (100% used CCT < 3h/day), and the predominance of physical inactivity (51% of the individuals practiced < 150 min/week of physical activities in total). Cluster 3 was characterized by a prolonged use of CCT during leisure (100% used CCT ≥ 3 h/day) and a predominance of active individuals (66% of the individuals practiced ≥ 150 min/week of physical activities in total, this being the cluster with a greater commitment of individuals to activities during leisure time and when adding leisure to commuting time). Cluster 4 was characterized by individuals without prolonged exposure to screens during leisure (100% of the individuals used TV or CCT < 3h/day) and a predominance of active individuals (57% practiced ≥ 150 min/week of physical activities in total). In the analysis stratified by sex, for men, in comparison to women, cluster 2 differed in frequency of prolonged TV use (48.2% vs. 100%) and of physical activities in total (55.4% vs. 46.2%). Cluster 3 showed a difference in frequency of physical activity in both leisure time and while commuting (60.1% vs. 48.3%) (especially leisure (46.9% vs. 31.6%)) and, consequently, of physical activities in total (72.7% vs. 61.0%) (Table 3). In the analysis of the total population, considering cluster 1 as a reference, cluster 2 showed a direct association with the female sex (OR: 2.78; p<.001); age ≥ 35 years (with higher magnitude among those who were ≥ 60 years of age (OR: 8.41; p<.001)); education ≥ 12 years (OR: 1.29; p<.05); per capita income ≥ 5 MS (OR: 1.64; p<.001); obesity (OR: 1.32; p<.05); diabetes, arterial hypertension, and other cardiovascular diseases (OR: 1.78; 1.27 and 1.52; respectively, p<.001); and an inverse association with per capita income of 1 to 2.99 MW (OR: 0.79; p<.001). Cluster 3 revealed a direct association with the female sex (OR: 2.47; p<.001); education ≥ 9 years (higher in the group ≥ 12 years of age (OR: 3.22; p<.001)); income ≥ 3 MW (higher for ≥ 5 NW (OR: 2.11; p<.001)); low weight (OR: 1.85; p<.05); and obesity (OR: 1.31; p<.001), as well as diabetes (OR: 1.34; p<.05) and other cardiovascular diseases (OR: 1.41; p<.05). Cluster 3 presented an inverse association with age ≥ 35 (especially among those who were ≥ 60 (OR: 0.47; p< .001)). Cluster 4 presented a direct association with the female sex (OR: 2.56; p<.001); age ≥ 60 (OR: 4.81; p<.001); education ≥ 9 years (higher in the group ≥ 12 years of age (OR: 2.53; p<.001)); income ≥ 5 MS (OR: 1.94; p<.001); diabetes (OR: 1.46; p<.001); arterial hypertension (OR: 1.11; p<.05); and other cardiovascular diseases (OR: 1.26; p<.05). Cluster 4 presented an inverse association with per capita income of 1 to 2.99 MW (OR: 0.87; p<.05). In the stratification by sex, important differences were observed in associations regarding education (cluster 2: direct association with more education among men and inverse association among women) and per capita income (cluster 3: direct association with the 1 to 2.99 MW bracket among men and inverse among women). There were also specific differences for obesity (direct association with 2 and 3 only among men, and an inverse association with cluster 4 only among women); diabetes (direct association with clusters 3 and 4 only among men); and arterial hypertension (direct association with clusters 2 and 4 only among women) (Table 4). DISCUSSION This study identified four clusters related to leisure screen time and to the practice of leisure-time physical activities in a representative sample of the adult population of a middle-income country, and its associated socioeconomic, demographic, and health factors. Cluster 1, which represents the smallest portion of the population, grouped Brazilians with a low time of exposure to TV and CCT, and who were also physically active – given that physical activities at work contributed by itself to achieving the weekly recommendation of physical activities. Moreover, these individuals showed more time on average spent in activities during commuting and at home in comparison to all of the other clusters. Nearly one third of the population was distributed between clusters 2 and 3, both with prolonged leisure screen time, but with distinctive profiles: cluster 2 was characterized by the huge prevalence of physical inactivity, while cluster 3 had individuals who were predominantly active, especially during leisure time – given that this group had the highest average time dedicated to this practice. Cluster 4, which represented the largest portion of the sample, presented the lowest average times of TV and CCT use, but they showed a minimal commitment to physical activity by specific domain. However, they were predominantly active when considering total physical activity. It is important to highlight that this cluster concentrated a large portion of Brazilians who did not meet the recommended amount of weekly physical activity. Sex, age, education, per capita income, nutritional status, and arterial hypertension were important factors in the heterogeneity of these clusters. Since studies on the co-occurrence of sedentary behavior and practice of physical activity among adults are focused on higher income populations 17 , 18 , comparing our results to those studies requires caution because of the socioeconomic and demographic differences between the studied populations. Furthermore, differences were also observed in the indicators used (types/domains of the behaviors, such as leisure screen time vs. time sitting down 17 , 18 ; physical activities by domain vs. total physical activity 17 , 18 and/or in three domains (leisure, commuting, and work 18 )); and in the characterization of the clusters (variations in the cutoff points used to classify the behaviors). Clusters 1 and 2 showed similarities with profiles identified in studies with representative samples of the adult population in France 18 and with that of adults in the United Kingdom 17 . Cluster 1 corresponds to the profile “little time spent sitting down and a high level of physical activity (high at work 18 )”; meanwhile, cluster 2 is similar to the profile “long time sitting down and low level of physical activity (low during leisure time 18 )”. Clusters 3 and 4 distinguished themselves from the mixed behavior detected in these European studies, which identified a “moderate amount of time sitting down and high physical activity (moderate at work)”, “a short time sitting down and a low level of physical activities (low during commuting)”, and “a short time sitting down and a low level of physical activity” among French adults, as compared to “a moderate amount of time sitting down and a moderate level of physical activities” among English adults. Men with lower socioeconomic status were associated with the cluster of low sedentary behavior and were active, especially at work, both in the present study (cluster 1) and in the similar analysis conducted in France 18 . In Brazil, clusters of physically active workers presented more practice of physical activities in all of the domains, except during leisure time. By contrast, in France, a greater commitment to activities was observed in commuting and leisure time. Similar results were found in a study conducted with populations of South America, including Brazil, which demonstrated a higher level of physical activities at work among men, as well as an association between less education and more practice of physical activities in different domains, except in leisure 14 . These findings reveal social iniquities in our country as compared to European countries, in which programs of promotion of healthy lifestyles have invested in opportunities for access, choices, and motivation for the entire population to practice leisure-time physical activities 18 . Barriers for the practice of leisure-time physical activities go beyond the overload that occurs in other domains and needs to be addressed by public policies. Women and older individuals presented more exposure to the combination of risk behaviors in the present study (cluster 2), given that this finding was also observed in the population of the United Kingdom 17 . However, this cluster has also been associated to higher socioeconomic status, similar to the population of France 18 . When we stratified by sex, an association was found between higher education and higher income among men, but not among women. Corroborating these results, evidence showed that spending more time watching TV was more common among women 26 and was associated with an increase in age 10 , 27 and a lower socioeconomic status 10 , 26 , 28 . Furthermore, we observed that, even in Brazil, higher education was associated with lower levels of physical activities among men and among the young and middle-aged groups, but not among women and among the oldest 14 . The World Health Survey indicated that being female and having more education and income was associated with less practice of physical activities 13 . Additionally, the presence of obesity and NCDs was more strongly associated with cluster 2. Evidence has shown that greater sedentary behavior, especially increased TV 7 time, together with physical inactivity 5 , tend to lead to an increased risk for cardiovascular diseases, arterial hypertension, and type 2 diabetes, with more damaging effects when combined 5 , 8 . These behaviors have usually been associated with more adiposity and weight (limited evidence) 4 , 5 , which favor the occurrence of NCDs 29 . Meanwhile, younger individuals, with higher educational and income levels were more heavily associated with cluster 3, which also showed associations with being female, obesity, and NCDs. In this cluster, we noticed stronger associations with the younger individuals and with more years of education among men in comparison to women. Our associations are consistent with previous studies, which revealed that the use of other screens (like CCT) was associated with younger individuals 27 , 28 and with a higher level of education 30 , while the practice of leisure-time physical activities was more strongly associated with men with a higher education 14 . In addition, income was associated differently between sexes, showing direct associations with income brackets from 1 MW among men, but only in the highest range for women. Furthermore, obesity and NCDs were associated with men but not with women. It is important to highlight that cluster 3 refuted the displacement hypothesis, since sedentary behavior, including screen time, does not seem to displace physical activities of moderate to vigorous intensity 31 , especially regarding leisure-time activities. Although screen devices (mobile devices like the CCT cluster or non-mobile devices like TV-cluster 2) may have a direct impact on the practice of leisure-time physical activities. It is important to emphasize that more healthy behavior must be sought as a protection against NCDs. As observed in cluster 4, there is a need to aggregate a greater scope of the recommended weekly practice of physical activities, especially in the discretionary domains, which will allow one to reach a larger portion of Brazilians, given its population scope. This cluster also showed associations with women and individuals of lower socioeconomic status with the presence of NCDs, as found in clusters 2 and 3; however, the strength of the association and the stratification by sex enabled elucidations concerning the difference between them. Promoting healthier lifestyles is a worldwide goal for combating NCDs. Among the domains of physical activity, leisure stands out for its discretionary nature, configuring itself as a strategic target for public health directives aimed at decreasing sedentary behavior and the encouragement of the regular practice of physical activities. In addition to leisure-time physical activities, there is also an urgent need to integrate the multiple benefits of active commuting (walking and cycling) and to incorporate recommendations to the guidelines of sedentary behavior and physical activities in an attempt to improve public health globally 32 . Finally, even though there are positive associations between physical activities at work and health outcomes (mainly related to cancer, cardiovascular disease, and mental health), evidence is inconclusive regarding benefits comparable to those of leisure-time physical activities 33 . Disfavorable evidence, regarding high physical activities at work and mortality by all causes among men, as well as mental health and sleep disorders, have been reported 33 . This paradox of physical activities elucidates the heterogeneity of evidence and highlights the need for better quality evidence 33 , which takes into consideration the compositional nature of physical activities, thus obtaining an unequivocal statement on the health effects of physical activities at work. However, the benefits inherent to the practice of leisure-time physical activities must be considered, as it is associated with a personal choice, dependent on the availability of time and access to appropriate spaces for its practice, and reflects on one’s well-being and quality of life. Our study presents some limitations that should be considered. First, the measures of leisure screen time were self-reported and may have been affected by memory and information bias, consequently interfering in the estimates. However, self-reported questionnaires are widely used in epidemiological research because of their viability for population monitoring and applicability in large samples 34 , as well as their capacity to capture information about type/domain of behaviors. The second limitation is related to the presence of outliers in a portion of the sample. However, the statistical treatment of the data allowed these individuals to remain in the sample, preserving their representativeness and ensuring the agreement of the final grouping solutions. Conclusion This study identified four clusters of leisure screen time and leisure-time physical activities in the Brazilian population. Two clusters showed a prolonged use of screens but differed regarding the practice of physical activities, given that one showed a predominance of inactive individuals and the other a predominance of active individuals (especially during leisure time). The other two clusters showed a low use of screens connected to active individuals (especially at work) or a majority of physically active individuals. Socioeconomic and demographic factors, as well as health factors associated with the clusters, provide subsidies for the design of public health policies, with specific approaches and target interventions, seeking to promote healthier lifestyles and a population that is less exposed to screens and more active. Declarations Ethics approval and consent to participate: Not applicable Consent for publication: Not applicable Availability of data and materials: Not applicable Competing interests : The authors declare no conflicts of interest. Funding: The work was supported by the Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES) [grant number 001] and the Fundação de Amparo à Pesquisa do Estado de Minas Gerais (Fapemig). Authors' contributions: Authors PCC, TMS, and RMC conceptualized the study, conducted the statistical analyses, interpreted the data, and drafted and revised the manuscript. Authors MMS, EGM, and TCS contributed to data interpretation and critically reviewed the manuscript for important intellectual content. All authors approved the final version to be published. Acknowledgment: The authors would like to thank the Federal University of Juiz de Fora, the State University of Rio de Janeiro, and the Federal University of Minas Gerais for their institutional and academic support throughout the development of this work. This study was supported by the Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES) [grant number 001] and the Fundação de Amparo à Pesquisa do Estado de Minas Gerais (Fapemig), whose financial support was essential for the completion of this research. References Bull FC, Al-Ansari SS, Biddle S, et al. World Health Organization 2020 guidelines on physical activity and sedentary behaviour. Br J Sports Med. 2020;54(24):1451-1462. doi:10.1136/bjsports-2020-102955 World Health Organization. Global Action Plan for the Prevention and Control of Noncommunicable Diseases 2013-2020. World Health Organization; 2013. Global action plan on physical activity 2018–2030: more active people for a healthier world. Geneva: World Health Organization; 2018. doi:10.2471/BLT.20.250936 World Health Organization. WHO Guidelines on Physical Activity and Sedentary Behaviour. Geneva: World Health Organization; 2020. Physical Activity Guidelines Advisory Committee. Physical Activity Guidelines Advisory Committee Scientific Report. Washington, DC: U.S. Department of Health and Human Services, 2018. Saunders TJ, McIsaac T, Douillette K, et al. Sedentary behaviour and health in adults: an overview of systematic reviews. Appl Physiol Nutr Metab. 2020;45(10 (Suppl. 2)):S197-S217. doi:10.1139/apnm-2020-0272 Patterson R, McNamara E, Tainio M, et al. Sedentary behaviour 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. doi:10.1007/s10654-018-0380-1 Ekelund U, Steene-Johannessen J, Brown WJ, et al. Does physical activity attenuate, or even eliminate, the detrimental association of sitting time with mortality? A harmonised meta-analysis of data from more than 1 million men and women. The Lancet. 2016;388(10051):1302-1310. doi:10.1016/S0140-6736(16)30370-1 Guthold R, Stevens GA, Riley LM, Bull FC. Worldwide trends in insufficient physical activity from 2001 to 2016: a pooled analysis of 358 population-based surveys with 1·9 million participants. Lancet Glob Health. 2018;6(10):e1077-e1086. doi:10.1016/S2214-109X(18)30357-7 Bauman AE, Petersen CB, Blond K, Rangul V, Hardy LL. The Descriptive Epidemiology of Sedentary Behaviour. In: Leitzmann MF, Jochem C, Schmid D, eds. Sedentary Behaviour Epidemiology. Springer Series on Epidemiology and Public Health. Springer International Publishing; 2018:73-106. doi:10.1007/978-3-319-61552-3_4 Instituto Brasileiro de Geografia e Estatística (IBGE). Pesquisa Nacional de Saúde 2019: percepção do estado de saúde, estilos de vida, doenças crônicas e saúde bucal: Brasil e grandes regiões. IBGE, Coordenação de Trabalho e Rendimento. Rio de Janeiro, 2020. 113p. Brasil. Brasil. Ministério da Saúde. Secretaria de Vigilância em Saúde. Departamento de Análise em Saúde e Vigilância de Doenças Não Transmissíveis. Vigitel Brasil 2019: 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 2019 [recurso eletrônico] / Ministério da Saúde, Secretaria de Vigilância em Saúde, Departamento de Análise em Saúde e Vigilância de Doenças não Transmissíveis. – Brasília: Ministério da Saúde, 2020. 137p. Koyanagi A, Stubbs B, Vancampfort D. Correlates of low physical activity across 46 low- and middle-income countries: A cross-sectional analysis of community-based data. Prev Med. 2018;106:107-113. doi:10.1016/j.ypmed.2017.10.023 Werneck AO, Baldew SS, Miranda JJ, et al. Physical activity and sedentary behavior patterns and sociodemographic correlates in 116,982 adults from six South American countries: the South American physical activity and sedentary behavior network (SAPASEN). Int J Behav Nutr Phys Act. 2019;16(1):68. doi:10.1186/s12966-019-0839-9 Werneck AO, Barboza LL, Araújo RHO, et al. Time Trends and Sociodemographic Inequalities in Physical Activity and Sedentary Behaviors Among Brazilian Adults: National Surveys from 2003 to 2019. J Phys Act Health. 2021;18(11):1332-1341. doi:10.1123/jpah.2021-0156 Werneck AO, Araujo RH, Aguilar-Farias N, et al. Time trends and inequalities of physical activity domains and sitting time in South America. J Glob Health. 2022;12:04027. doi:10.7189/jogh.12.04027 Zwolinsky S, McKenna J, Pringle A, et al. Physical Activity and Sedentary Behavior Clustering: Segmentation to Optimize Active Lifestyles. J Phys Act Health. 2016;13(9):921-928. doi:10.1123/jpah.2015-0307 Omorou AY, Coste J, Escalon H, Vuillemin A. Patterns of physical activity and sedentary behaviour in the general population in France: cluster analysis with personal and socioeconomic correlates. J Public Health. 2016;38(3):483-492. doi:10.1093/pubmed/fdv080 Manta SW, Sandreschi PF, Matias TS, Tomicki C, Benedetti TRB. Clustering of Physical Activity and Sedentary Behavior Associated to Risk for Metabolic Syndrome in Older Adults. J Aging Phys Act. 2019;27(6):781-786. doi:10.1123/japa.2018-0300 Werneck AO, Silva DR, Malta DC, et al. Changes in the clustering of unhealthy movement behaviors during the COVID-19 quarantine and the association with mental health indicators among Brazilian adults. Transl Behav Med. 2021;11(2):323-331. doi:10.1093/tbm/ibaa095 World Health Organization. Global Status Report on Physical Activity 2022. Geneva: World Health Organization; 2022. Stopa SR, Szwarcwald CL, Oliveira MM de, et al. Pesquisa Nacional de Saúde 2019: histórico, métodos e perspectivas. Epidemiol E Serviços Saúde. 2020;29(5):e2020315. doi:10.1590/s1679-49742020000500004 United Nations Development Programme, Fundação João Pinheiro, Instituto de Pesquisa Econômica Aplicada, eds. Desenvolvimento Humano Nas Macrorregiões Brasileiras. Primeira edição. PNUD Brasil; 2016. WHO Expert Committee on Physical Status: the Use and Interpretation of Anthropometry, ed. Physical Status: The Use and Interpretation of Anthropometry: Report of a WHO Expert Committee. World Health Organization; 1995. Ross R, Chaput JP, Giangregorio LM, et al. Canadian 24-Hour Movement Guidelines for Adults aged 18–64 years and Adults aged 65 years or older: an integration of physical activity, sedentary behaviour, and sleep. Appl Physiol Nutr Metab. 2020;45(10 (Suppl. 2)):S57-S102. doi:10.1139/apnm-2020-0467 O’Donoghue G, Perchoux C, Mensah K, 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(1):163. doi:10.1186/s12889-016-2841-3 Prince SA, Melvin A, Roberts KC, Butler GP, Thompson W. Sedentary behaviour surveillance in Canada: trends, challenges and lessons learned. Int J Behav Nutr Phys Act. 2020;17(1):34. doi:10.1186/s12966-020-00925-8 Silva DR, Collings P, Araujo RHO, Barboza LL, Szwarcwald CL, Werneck AO. Correlates of screen-based behaviors among adults from the 2019 Brazilian National Health Survey. BMC Public Health. 2021;21(1):2289. doi:10.1186/s12889-021-12340-0 World Health Organization. Noncommunicable Diseases Country Profiles 2018. World Health Organization; 2018. Prince SA, Roberts KC, Melvin A, Butler GP, Thompson W. Gender and education differences in sedentary behaviour in Canada: an analysis of national cross-sectional surveys. BMC Public Health. 2020;20(1):1170. doi:10.1186/s12889-020-09234-y Mansoubi M, Pearson N, Biddle SJH, Clemes S. The relationship between sedentary behaviour and physical activity in adults: A systematic review. Prev Med. 2014;69:28-35. doi:10.1016/j.ypmed.2014.08.028 Jochem C, Leitzmann M. A call for integrating active transportation into physical activity and sedentary behaviour guidelines. Lancet Planet Health. 2023;7(2):e112-e113. doi:10.1016/S2542-5196(23)00001-3 Cillekens B, Lang M, Van Mechelen W, et al. How does occupational physical activity influence health? An umbrella review of 23 health outcomes across 158 observational studies. Br J Sports Med. 2020;54(24):1474-1481. doi:10.1136/bjsports-2020-102587 Riley L, Guthold R, Cowan M, et al. The World Health Organization STEPwise Approach to Noncommunicable Disease Risk-Factor Surveillance: Methods, Challenges, and Opportunities. Am J Public Health. 2016;106(1):74-78. doi:10.2105/AJPH.2015.302962 Prochaska JJ, Spring B, Nigg CR. Multiple health behavior change research: An introduction and overview. Prev Med. 2008;46(3):181-188. doi:10.1016/j.ypmed.2008.02.001 Mekary RA, Willett WC, Hu FB, Ding EL. Isotemporal Substitution Paradigm for Physical Activity Epidemiology and Weight Change. Am J Epidemiol. 2009;170(4):519-527. doi:10.1093/aje/kwp163 Chastin SFM, Palarea-Albaladejo J, Dontje ML, Skelton DA. Combined Effects of Time Spent in Physical Activity, Sedentary Behaviors and Sleep on Obesity and Cardio-Metabolic Health Markers: A Novel Compositional Data Analysis Approach. Devaney J, ed. PLOS ONE. 2015;10(10):e0139984. doi:10.1371/journal.pone.0139984 Dumuid D, Pedišić Ž, Stanford TE, et al. The compositional isotemporal substitution model: A method for estimating changes in a health outcome for reallocation of time between sleep, physical activity and sedentary behaviour. Stat Methods Med Res. 2019;28(3):846-857. doi:10.1177/0962280217737805 Chastin SFM, McGregor DE, Biddle SJH, et al. Striking the Right Balance: Evidence to Inform Combined Physical Activity and Sedentary Behavior Recommendations. J Phys Act Health. 2021;18(6):631-637. doi:10.1123/jpah.2020-0635 Curtis RG, Dumuid D, Olds T, et al. The Association Between Time-Use Behaviors and Physical and Mental Well-Being in Adults: A Compositional Isotemporal Substitution Analysis. J Phys Act Health. 2020;17(2):197-203. doi:10.1123/jpah.2018-0687 Dumuid D, Olds T, Wake M, et al. Your best day: An interactive app to translate how time reallocations within a 24-hour day are associated with health measures. Harezlak J, ed. PLOS ONE. 2022;17(9):e0272343. doi:10.1371/journal.pone.0272343 Tables Tables 1 to 4 are available in the Supplementary Files section Chart 1 Chart 1 is available in the Supplementary Files section. Additional Declarations No competing interests reported. Supplementary Files Chart1.docx Tables.docx Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. 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Prolonged time spent in sedentary activities, especially watching television (TV), is associated with the increased risk of adverse health outcomes, related to Noncommunicable Diseases (NCDs)\u003csup\u003e\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e,\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e,\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e\u003c/sup\u003e, to mental health\u003csup\u003e\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e\u003c/sup\u003e, and to all-cause mortality\u003csup\u003e\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e,\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e,\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e\u003c/sup\u003e. Evidence shows that risks are potentially higher when prolonged sedentary behavior and a lack of physical activity happen simultaneously\u003csup\u003e\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e,\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e\u003c/sup\u003e. Despite this, the prevalence of sedentary behaviors has been increasing\u003csup\u003e\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e\u003c/sup\u003e and the prevalence of physical activity still remains high\u003csup\u003e\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e,\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e\u003cp\u003eEstimates from the last decade showed that one in every four adults, in high income countries\u003csup\u003e\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e\u003c/sup\u003e, as well as in countries like Brazil\u003csup\u003e\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e,\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e\u003c/sup\u003e, reported watching TV for three hours or more per day. Adding other screens to this figure \u0026ndash; such as cell phone, computer, or tablet (CCT) for leisure purposes \u0026ndash; increases this frequency considerably. Among Brazilian adults residing in state capitals and the Federal District, 62.7% reported spending three hours or more per day watching TV or using CCT for leisure in 2019\u003csup\u003e12\u003c/sup\u003e. Studies have shown a decrease in the habit of watching TV, as compared to an increase in the prevalence of the prolonged use of CCT for leisure among Brazilians over time (2008/19)\u003csup\u003e15\u0026ndash;16\u003c/sup\u003e. In parallel, the frequency of adults who do not meet the current recommendation of physical activities from the World Health Organization (WHO) (150\u0026ndash;300 minutes/week of aerobic activity of moderate intensity, or the equivalent in vigorous activity\u003csup\u003e\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e\u003c/sup\u003e) was 27.5% in 2016, globally\u003csup\u003e\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e\u003c/sup\u003e, and 40.3% in 2019, in Brazil (considering leisure, commuting, and work domains)\u003csup\u003e\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e\u003c/sup\u003e. Although the prevalence of physical activities in leisure time and in total (especially by the contribution of leisure-time physical activities, as well as by physical activities at work) increased among Brazilians from 2003 to 2019, an increase in social inequities was revealed as well\u003csup\u003e\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e\u003cp\u003eStudies with populations from low- and middle-income countries have mainly investigated one or more of these behaviors in an isolated manner, such as the amount of time sitting down, the amount of time in front of a screen, total physical activity, and/or by domains, and its associations with sociodemographic factors\u003csup\u003e\u003cspan additionalcitationids=\"CR14 CR15\" citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e\u003c/sup\u003e. Few studies have investigated the co-occurrence of these behaviors and associated factors. Behavior patterns which are potentially prejudicial to health (particularly related to time sitting down and to total physical activity and/or by domains) were identified in high-income countries, given that sex, age, and socioeconomic status were important determinants\u003csup\u003e\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e,\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e\u003c/sup\u003e. Studies of this nature in Brazil are scarce, and the findings were limited to a non-probabilistic sub-sample\u003csup\u003e\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e\u003c/sup\u003e (elderly) or to atypical periods (compulsory confinement during the COVID-19 pandemic), with methodological differences\u003csup\u003e\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e\u003c/sup\u003e. The lack of evidence regarding behavior patterns related to different types/domains of sedentary behavior and physical activities, as well as associated factors, reveals gaps that need to be filled\u003csup\u003e\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e\u003cp\u003eThe identification of clusters of these behaviors in a representative sample of the population of a middle-income country, as well as the socioeconomic, demographic, and health factors, which differ among lifestyles that are somewhat healthy, allows for a better understanding and helps to develop and prioritize more efficient interventions, Therefore, the objective of this study is to analyze the co-occurrence of the use of screens and physical activities in the adult Brazilian population, together with its association with socioeconomic and demographic characteristics, nutritional status, and chronic health conditions.\u003c/p\u003e"},{"header":"METHODOLOGY","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e\u003ch2\u003eStudy design\u003c/h2\u003e\u003cp\u003eThis is a cross-sectional study conducted with data from the 2019 National Health Survey (\u003cem\u003ePesquisa Nacional de Sa\u0026uacute;de\u003c/em\u003e - PNS), a household survey representative of the Brazilian population conducted by the Ministry of Health and the Brazilian Institute of Geography and Statistics, every five years\u003csup\u003e\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e\u003c/div\u003e\n\u003ch3\u003eSampling and data collection\u003c/h3\u003e\n\u003cp\u003eThe 2019 PNS uses cluster sampling in three stages: in the first, all of the Primary Sampling Units (PSU) were selected, made up of census sectors or groups of sectors. In the second stage, a particular permanent household was chosen from each PSU. Finally, in the third stage, a resident, aged 15 years or older, was randomly chosen among the eligible members of that household\u003csup\u003e\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e,\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e\u003c/sup\u003e. The interviews were conducted between August 2019 and March 2020, by means of mobile devices (smartphones) for the collection of information regarding health determinants and conditioning factors of the population\u003csup\u003e\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e,\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e\u003c/sup\u003e. The sample included 8,036 PSUs, 108,525 households, and 90,846 residents, aged 15 years or older, with residences throughout Brazil\u003csup\u003e\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e\u003c/sup\u003e. For the current study, a sub-sample was used, comprised exclusively of residents, aged 18 years or older, totalling 88,531 adults.\u003c/p\u003e\u003cp\u003eThe data from the PNS was weighted by means of sampling weights, for both households and selected residents. These weights were calculated based on the inverse of the product of the selection probabilities in each of the three sampling stages, incorporating a correction factor for losses and making adjustments to the population totals\u003csup\u003e\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e,\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e\u003cp\u003eMore details about the methodological procedures, questionnaire, and database of the 2019 PNS are available in the electronic site\u0026thinsp;\u0026lt;\u0026thinsp;\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.pns.icict.fiocruz.br/%3E\u003c/span\u003e\u003cspan address=\"https://www.pns.icict.fiocruz.br/%3E\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/p\u003e\n\u003ch3\u003eVariables and indicators\u003c/h3\u003e\n\u003cp\u003eThe indicators of sedentary behavior (TV and CCT time) during leisure time and during the practice of physical activities in the four domains (leisure, commuting, home, and work) included in this study are described in Chart \u003cspan refid=\"Str1\" class=\"InternalRef\"\u003e1\u003c/span\u003e. Additionally, derived indicators were considered: the sum of TV and CCT times, the sum of physical activity in the domains of leisure and commutting, and the total amount of physical activity in the four domains together.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eThe socioeconomic and demographic, nutritional status, and chronic health condition variables, which complemented the analyses, were: sex (male | female); age group in years (18\u0026ndash;34 | 35\u0026ndash;59 | \u0026ge; 60); education in years of schooling (0\u0026ndash;8 | 9\u0026ndash;11 | \u0026ge; 12); per capita income in Minimum Wage units (MW) (*\u0026lt; 1 MW (from R\u003cspan\u003e$\u003c/span\u003e0 to R\u003cspan\u003e$\u003c/span\u003e 997 reais) | \u0026ge; 1 MW to \u0026lt;\u0026thinsp;3 MW (R\u003cspan\u003e$\u003c/span\u003e998 to R\u003cspan\u003e$\u003c/span\u003e2993) | \u0026ge; 3 to \u0026lt;\u0026thinsp;5 MW (R\u003cspan\u003e$\u003c/span\u003e2994 to R\u003cspan\u003e$\u003c/span\u003e4989) | \u0026ge; 5 MW (R\u003cspan\u003e$\u003c/span\u003e4990)), Body Mass Index (BMI) (\u0026lt;\u0026thinsp;18.5: low weight | \u0026ge; 18.5 and \u0026lt;\u0026thinsp;25 kg/m\u003csup\u003e2\u003c/sup\u003e: eutrophic | \u0026ge; 25 and \u0026lt;\u0026thinsp;30 kg/m\u003csup\u003e2\u003c/sup\u003e: pre-obesity | \u0026ge; 30 kg/m\u003csup\u003e2\u003c/sup\u003e: obesity\u003csup\u003e\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e\u003c/sup\u003e); and chronic health conditions (diabetes | arterial hypertension | other cardiovascular diseases, as well as stroke, acute myocardial infarction, angina, and heart failure).\u003c/p\u003e\u003cdiv id=\"Sec6\" class=\"Section2\"\u003e\u003ch2\u003eData analysis\u003c/h2\u003e\u003cp\u003eMeasures of central tendency and dispersion of indicators of screen time and physical activity time were estimated for the entire population and stratified by sex.\u003c/p\u003e\u003cp\u003eCluster analysis was employed for the identification of co-occurrence (or clustering) of screen time and physical activities by the use of the K-means non-hierarchical method. The cluster analysis was conducted with six indicators: TV and CCT time during leisure, and time of physical activities in the four domains: leisure, commuting, home, and work, for the total population and by sex. The unit of time spent on each type of behavior was converted into minutes/week for the cluster analysis, and was later presented in its original unit to facilitate the interpretation of the results. Since cluster analyses are sensitive to outliers, these were identified in the physical activity variables for the four domains (corresponding to approximately 15% of the total population). These observations had their values replaced by the mean value plus two standard deviations (adopted as maximum value - cutoff point) for each indicator.\u003c/p\u003e\u003cp\u003eThe choice of the number of clusters for the total population and by sex was based on the Calinski/Harabasz pseudo-F evaluation criteria between analyses with 2, 3, 4, 5, and 6 solutions. The reliability and stability of the number of final solutions were tested in repeated analyses of three random subsamples (equivalent to 50%) stemming from the total sample (of the total population and for each sex). The cluster analysis is considered satisfactory when the results obtained for the subsamples are similar to what is found for the total sample\u003csup\u003e\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e\u003c/sup\u003e. The \u003cem\u003eKappa\u003c/em\u003e coefficient was calculated to analyze the agreement of the final cluster solution of each subsample with that of the total sample, and indicated excellent agreement (Kappa\u0026thinsp;\u0026gt;\u0026thinsp;0.99).\u003c/p\u003e\u003cp\u003eThe Levene test was conducted to evaluate the homogeneity of variances of the indicators of leisure screen time and leisure-time physical activity of the clusters. Next, the ANOVA and post hoc Bonferroni tests were conducted to reveal differences in the average time of these indicators between the clusters, for the total population and by sex.\u003c/p\u003e\u003cp\u003eTo characterize the clusters, the frequency of the indicator of each behavior was also used (\u0026ge;\u0026thinsp;3 h/day considered prolonged use of screen | \u0026lt; 3 h/day considered low use of screen\u003csup\u003e\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e\u003c/sup\u003e, and \u0026ge;\u0026thinsp;150 min/week of aerobic physical activity of moderate intensity (or the equivalent in vigorous activity) was considered to be active | \u0026lt; 150 min/week was considered to be insufficiently active\u003csup\u003e\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e\u003c/sup\u003e). Moreover, frequencies were estimated for new indicators, which included the sum of TV and CCT time (total screen time), the time spent on leisure-time physical activity and commuting (probable discretionary domains), and the time of physical activity in all of the domains\u003csup\u003e\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e\u003c/sup\u003e (total time of physical activity).\u003c/p\u003e\u003cp\u003eMultivariate multinomial logistic regression models were used to verify the associations between outcome variables (clusters) and explanatory variables (socioeconomic and demographic characteristics, nutritional status, and chronic health conditions). Initially, simple multinomial logistic regression models were constructed with each explanatory variable. Next, all variables that presented a p-value\u0026thinsp;\u0026le;\u0026thinsp;0.20 in the simple regression models were taken to the multivariate model, and their adjustment was performed using the backward method (p\u0026thinsp;\u0026lt;\u0026thinsp;0.05). The distribution (%) and the adjusted Odds Ratio (OR) for all of the explanatory variables included in this study were estimated. All of the analyses were conducted for the total population and stratified by sex.\u003c/p\u003e\u003cp\u003eThe analyses were conducted with the Stata version 16.1 app (StataCorp LP, College Station, USA), using the survey module for complex samples, which incorporates the weighting factors (except in the partition of groups in the cluster analysis).\u003c/p\u003e\u003c/div\u003e\n\u003ch3\u003eEthical aspects\u003c/h3\u003e\n\u003cp\u003e The 2019 PNS was approved by the National Research Ethics Committee of the National Health Council, from the Ministry of Health (protocol no. 3,529,376). A free and informed consent form was previously signed by the participants.\u003c/p\u003e"},{"header":"RESULTS","content":"\u003cp\u003eIn the total population, average TV time (2.2 h/day) was similar to CCT time during leisure (1.9 h/day). The practice of physical activities varied between the domains, with the highest average registered at work (296.2 min/week), followed by activities during leisure time (113.3 min/week), while commuting (84.7 min/week), and at home (42.6 min/week)). Compared to women, men spent more time in activities during leisure (133.6 \u003cem\u003evs.\u003c/em\u003e 95.4 min/week) and at work (418.2 \u003cem\u003evs.\u003c/em\u003e 188.7 min/week), and less time watching TV (2.2 \u003cem\u003evs.\u003c/em\u003e 2.3 h/day) and less involvement in activities at home (20.1 \u003cem\u003evs.\u003c/em\u003e 62.4 min/week) (Table 1).\u003c/p\u003e\n\u003cp\u003eFour distinct clusters of leisure screen time and leisure-time physical activities were identified in the total population and by sex. The comparison of the averages of the indicators between the clusters showed that individuals in cluster 1 (13.0% of the population) had the highest average times of physical activity while commuting (116.1 min/week), at home (51.0 min/week), and at work (1593.9 min/week). In cluster 2 (14,9%), the highest average time of exposure to TV (4.9 h/day) was observed, and individuals practiced less activities during leisure (83.7 min/week) and at home (37.7 min/week). Meanwhile, those in cluster 3 (18.0%) used more CCT during leisure time (5.1 h/day) and practiced more leisure-time physical activities (156.6 min/week). Finally, those in cluster 4 (54.1%) used the screen less during leisure time (1.5 h/day of TV and 1.1 h/day of CCT) and practiced more physical activities at work (58.6 min/week). The analysis stratified by sex revealed differences in the population distribution of the clusters (men had a higher representation in clusters 1 and 2 and lower in cluster 4, when compared to women) and especially, in averages of TV time (in cluster 2, women watched more TV than men) and of physical activity during leisure time and at work (in all the clusters, men practiced more activities than women), and at home (in all the clusters, women practiced more activities than men) (Table 2).\u003c/p\u003e\n\u003cp\u003eThe cluster classification analysis revealed that cluster 1 was characterized by a low leisure screen time (around 90% of the individuals used TV or CCT \u0026lt; 3h/day) and a high prevalence of active individuals (100% practiced ≥ 150 min/week of total physical activity, especially at the work domain). Cluster 2 showed a prolonged use of TV (100% watched TV ≥ 3 h/day), absence of prolonged exposure to CCT (100% used CCT \u0026lt; 3h/day), and the predominance of physical inactivity (51% of the individuals practiced \u0026lt; 150 min/week of physical activities in total). Cluster 3 was characterized by a prolonged use of CCT during leisure (100% used CCT ≥ 3 h/day) and a predominance of active individuals (66% of the individuals practiced ≥ 150 min/week of physical activities in total, this being the cluster with a greater commitment of individuals to activities during leisure time and when adding leisure to commuting time). Cluster 4 was characterized by individuals without prolonged exposure to screens during leisure (100% of the individuals used TV or CCT \u0026lt; 3h/day) and a predominance of active individuals (57% practiced ≥ 150 min/week of physical activities in total). In the analysis stratified by sex, for men, in comparison to women, cluster 2 differed in frequency of prolonged TV use (48.2%\u0026nbsp;\u003cem\u003evs.\u003c/em\u003e 100%) and of physical activities in total (55.4% \u003cem\u003evs.\u003c/em\u003e 46.2%). Cluster 3 showed a difference in frequency of physical activity in both leisure time and while commuting (60.1% \u003cem\u003evs.\u003c/em\u003e 48.3%) (especially leisure (46.9% \u003cem\u003evs.\u003c/em\u003e 31.6%)) and, consequently, of physical activities in total (72.7% \u003cem\u003evs.\u003c/em\u003e 61.0%) (Table 3).\u003c/p\u003e\n\u003cp\u003eIn the analysis of the total population, considering cluster 1 as a reference, cluster 2 showed a direct association with the female sex (OR: 2.78; p\u0026lt;.001); \u0026nbsp;age ≥ 35 years (with higher magnitude among those who were ≥ 60 years of age (OR: 8.41; p\u0026lt;.001)); education ≥ 12 years (OR: 1.29; p\u0026lt;.05); per capita income ≥ 5 MS (OR: 1.64; p\u0026lt;.001); obesity (OR: 1.32; p\u0026lt;.05); diabetes, arterial hypertension, and other cardiovascular diseases (OR: 1.78; 1.27 and 1.52; respectively, p\u0026lt;.001); and an inverse association with per capita income of 1 to 2.99 MW (OR: 0.79; p\u0026lt;.001). Cluster 3 revealed a direct association with the female sex (OR: 2.47; p\u0026lt;.001); education ≥ 9 years (higher in the group ≥ 12 years of age (OR: 3.22; p\u0026lt;.001)); income ≥ 3 MW (higher for ≥ 5 NW (OR: 2.11; p\u0026lt;.001)); \u0026nbsp;low weight (OR: 1.85; p\u0026lt;.05); and obesity (OR: 1.31; p\u0026lt;.001), as well as diabetes (OR: 1.34; p\u0026lt;.05) and other cardiovascular diseases (OR: 1.41; p\u0026lt;.05). Cluster 3 presented an inverse association with age ≥ 35 (especially among those who were ≥ 60 (OR: 0.47; p\u0026lt; .001)). Cluster 4 presented a direct association with the female sex (OR: 2.56; p\u0026lt;.001); age ≥ 60 (OR: 4.81; p\u0026lt;.001); education ≥ 9 years (higher in the group ≥ 12 years of age (OR: 2.53; p\u0026lt;.001)); income ≥ 5 MS (OR: 1.94; p\u0026lt;.001); diabetes (OR: 1.46; p\u0026lt;.001); arterial hypertension (OR: 1.11; p\u0026lt;.05); and other cardiovascular diseases (OR: 1.26; p\u0026lt;.05). Cluster 4 presented an inverse association with per capita income of 1 to 2.99 MW (OR: 0.87; p\u0026lt;.05). In the stratification by sex, important differences were observed in associations regarding education (cluster 2: direct association with more education among men and inverse association among women) and per capita income (cluster 3: direct association with the 1 to 2.99 MW bracket among men and inverse among women). \u0026nbsp; There were also specific differences for obesity (direct association with 2 and 3 only among men, and an inverse association with cluster 4 only among women); diabetes (direct association with clusters 3 and 4 only among men); and arterial hypertension (direct association with clusters 2 and 4 only among women) (Table 4).\u003c/p\u003e"},{"header":"DISCUSSION","content":"\u003cp\u003eThis study identified four clusters related to leisure screen time and to the practice of leisure-time physical activities in a representative sample of the adult population of a middle-income country, and its associated socioeconomic, demographic, and health factors. Cluster 1, which represents the smallest portion of the population, grouped Brazilians with a low time of exposure to TV and CCT, and who were also physically active \u0026ndash; given that physical activities at work contributed by itself to achieving the weekly recommendation of physical activities. Moreover, these individuals showed more time on average spent in activities during commuting and at home in comparison to all of the other clusters. Nearly one third of the population was distributed between clusters 2 and 3, both with prolonged leisure screen time, but with distinctive profiles: cluster 2 was characterized by the huge prevalence of physical inactivity, while cluster 3 had individuals who were predominantly active, especially during leisure time \u0026ndash; given that this group had the highest average time dedicated to this practice. Cluster 4, which represented the largest portion of the sample, presented the lowest average times of TV and CCT use, but they showed a minimal commitment to physical activity by specific domain. However, they were predominantly active when considering total physical activity. It is important to highlight that this cluster concentrated a large portion of Brazilians who did not meet the recommended amount of weekly physical activity. Sex, age, education, per capita income, nutritional status, and arterial hypertension were important factors in the heterogeneity of these clusters.\u003c/p\u003e\u003cp\u003eSince studies on the co-occurrence of sedentary behavior and practice of physical activity among adults are focused on higher income populations\u003csup\u003e\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e,\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e\u003c/sup\u003e, comparing our results to those studies requires caution because of the socioeconomic and demographic differences between the studied populations. Furthermore, differences were also observed in the indicators used (types/domains of the behaviors, such as leisure screen time \u003cem\u003evs.\u003c/em\u003e time sitting down\u003csup\u003e\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e,\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e\u003c/sup\u003e; physical activities by domain \u003cem\u003evs.\u003c/em\u003e total physical activity\u003csup\u003e\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e,\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e\u003c/sup\u003e and/or in three domains (leisure, commuting, and work\u003csup\u003e\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e\u003c/sup\u003e)); and in the characterization of the clusters (variations in the cutoff points used to classify the behaviors).\u003c/p\u003e\u003cp\u003eClusters 1 and 2 showed similarities with profiles identified in studies with representative samples of the adult population in France\u003csup\u003e\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e\u003c/sup\u003e and with that of adults in the United Kingdom\u003csup\u003e\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e\u003c/sup\u003e. Cluster 1 corresponds to the profile \u0026ldquo;little time spent sitting down and a high level of physical activity (high at work\u003csup\u003e\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e\u003c/sup\u003e)\u0026rdquo;; meanwhile, cluster 2 is similar to the profile \u0026ldquo;long time sitting down and low level of physical activity (low during leisure time\u003csup\u003e\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e\u003c/sup\u003e)\u0026rdquo;. Clusters 3 and 4 distinguished themselves from the mixed behavior detected in these European studies, which identified a \u0026ldquo;moderate amount of time sitting down and high physical activity (moderate at work)\u0026rdquo;, \u0026ldquo;a short time sitting down and a low level of physical activities (low during commuting)\u0026rdquo;, and \u0026ldquo;a short time sitting down and a low level of physical activity\u0026rdquo; among French adults, as compared to \u0026ldquo;a moderate amount of time sitting down and a moderate level of physical activities\u0026rdquo; among English adults.\u003c/p\u003e\u003cp\u003eMen with lower socioeconomic status were associated with the cluster of low sedentary behavior and were active, especially at work, both in the present study (cluster 1) and in the similar analysis conducted in France\u003csup\u003e\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e\u003c/sup\u003e. In Brazil, clusters of physically active workers presented more practice of physical activities in all of the domains, except during leisure time. By contrast, in France, a greater commitment to activities was observed in commuting and leisure time. Similar results were found in a study conducted with populations of South America, including Brazil, which demonstrated a higher level of physical activities at work among men, as well as an association between less education and more practice of physical activities in different domains, except in leisure\u003csup\u003e\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e\u003c/sup\u003e. These findings reveal social iniquities in our country as compared to European countries, in which programs of promotion of healthy lifestyles have invested in opportunities for access, choices, and motivation for the entire population to practice leisure-time physical activities\u003csup\u003e\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e\u003c/sup\u003e. Barriers for the practice of leisure-time physical activities go beyond the overload that occurs in other domains and needs to be addressed by public policies.\u003c/p\u003e\u003cp\u003eWomen and older individuals presented more exposure to the combination of risk behaviors in the present study (cluster 2), given that this finding was also observed in the population of the United Kingdom\u003csup\u003e\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e\u003c/sup\u003e. However, this cluster has also been associated to higher socioeconomic status, similar to the population of France\u003csup\u003e\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e\u003c/sup\u003e. When we stratified by sex, an association was found between higher education and higher income among men, but not among women. Corroborating these results, evidence showed that spending more time watching TV was more common among women\u003csup\u003e\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e\u003c/sup\u003e and was associated with an increase in age\u003csup\u003e\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e,\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e\u003c/sup\u003e and a lower socioeconomic status\u003csup\u003e\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e,\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e,\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e\u003c/sup\u003e. Furthermore, we observed that, even in Brazil, higher education was associated with lower levels of physical activities among men and among the young and middle-aged groups, but not among women and among the oldest\u003csup\u003e\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e\u003c/sup\u003e. The World Health Survey indicated that being female and having more education and income was associated with less practice of physical activities\u003csup\u003e\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e\u003c/sup\u003e. Additionally, the presence of obesity and NCDs was more strongly associated with cluster 2. Evidence has shown that greater sedentary behavior, especially increased TV\u003csup\u003e7\u003c/sup\u003e time, together with physical inactivity\u003csup\u003e\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e\u003c/sup\u003e, tend to lead to an increased risk for cardiovascular diseases, arterial hypertension, and type 2 diabetes, with more damaging effects when combined\u003csup\u003e\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e,\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e\u003c/sup\u003e. These behaviors have usually been associated with more adiposity and weight (limited evidence)\u003csup\u003e\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e,\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e\u003c/sup\u003e, which favor the occurrence of NCDs\u003csup\u003e\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e\u003cp\u003eMeanwhile, younger individuals, with higher educational and income levels were more heavily associated with cluster 3, which also showed associations with being female, obesity, and NCDs. In this cluster, we noticed stronger associations with the younger individuals and with more years of education among men in comparison to women. Our associations are consistent with previous studies, which revealed that the use of other screens (like CCT) was associated with younger individuals\u003csup\u003e\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e,\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e\u003c/sup\u003e and with a higher level of education\u003csup\u003e\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e\u003c/sup\u003e, while the practice of leisure-time physical activities was more strongly associated with men with a higher education\u003csup\u003e\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e\u003c/sup\u003e. In addition, income was associated differently between sexes, showing direct associations with income brackets from 1 MW among men, but only in the highest range for women. Furthermore, obesity and NCDs were associated with men but not with women. It is important to highlight that cluster 3 refuted the displacement hypothesis, since sedentary behavior, including screen time, does not seem to displace physical activities of moderate to vigorous intensity\u003csup\u003e\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e\u003c/sup\u003e, especially regarding leisure-time activities. Although screen devices (mobile devices like the CCT cluster or non-mobile devices like TV-cluster 2) may have a direct impact on the practice of leisure-time physical activities. It is important to emphasize that more healthy behavior must be sought as a protection against NCDs.\u003c/p\u003e\u003cp\u003eAs observed in cluster 4, there is a need to aggregate a greater scope of the recommended weekly practice of physical activities, especially in the discretionary domains, which will allow one to reach a larger portion of Brazilians, given its population scope. This cluster also showed associations with women and individuals of lower socioeconomic status with the presence of NCDs, as found in clusters 2 and 3; however, the strength of the association and the stratification by sex enabled elucidations concerning the difference between them.\u003c/p\u003e\u003cp\u003ePromoting healthier lifestyles is a worldwide goal for combating NCDs. Among the domains of physical activity, leisure stands out for its discretionary nature, configuring itself as a strategic target for public health directives aimed at decreasing sedentary behavior and the encouragement of the regular practice of physical activities. In addition to leisure-time physical activities, there is also an urgent need to integrate the multiple benefits of active commuting (walking and cycling) and to incorporate recommendations to the guidelines of sedentary behavior and physical activities in an attempt to improve public health globally\u003csup\u003e\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e\u003c/sup\u003e. Finally, even though there are positive associations between physical activities at work and health outcomes (mainly related to cancer, cardiovascular disease, and mental health), evidence is inconclusive regarding benefits comparable to those of leisure-time physical activities\u003csup\u003e\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e\u003c/sup\u003e. Disfavorable evidence, regarding high physical activities at work and mortality by all causes among men, as well as mental health and sleep disorders, have been reported\u003csup\u003e\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e\u003c/sup\u003e. This paradox of physical activities elucidates the heterogeneity of evidence and highlights the need for better quality evidence\u003csup\u003e\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e\u003c/sup\u003e, which takes into consideration the compositional nature of physical activities, thus obtaining an unequivocal statement on the health effects of physical activities at work. However, the benefits inherent to the practice of leisure-time physical activities must be considered, as it is associated with a personal choice, dependent on the availability of time and access to appropriate spaces for its practice, and reflects on one\u0026rsquo;s well-being and quality of life.\u003c/p\u003e\u003cp\u003eOur study presents some limitations that should be considered. First, the measures of leisure screen time were self-reported and may have been affected by memory and information bias, consequently interfering in the estimates. However, self-reported questionnaires are widely used in epidemiological research because of their viability for population monitoring and applicability in large samples\u003csup\u003e\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e\u003c/sup\u003e, as well as their capacity to capture information about type/domain of behaviors. The second limitation is related to the presence of outliers in a portion of the sample. However, the statistical treatment of the data allowed these individuals to remain in the sample, preserving their representativeness and ensuring the agreement of the final grouping solutions.\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eThis study identified four clusters of leisure screen time and leisure-time physical activities in the Brazilian population. Two clusters showed a prolonged use of screens but differed regarding the practice of physical activities, given that one showed a predominance of inactive individuals and the other a predominance of active individuals (especially during leisure time). The other two clusters showed a low use of screens connected to active individuals (especially at work) or a majority of physically active individuals. Socioeconomic and demographic factors, as well as health factors associated with the clusters, provide subsidies for the design of public health policies, with specific approaches and target interventions, seeking to promote healthier lifestyles and a population that is less exposed to screens and more active.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate:\u0026nbsp;\u003c/strong\u003eNot applicable\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication:\u0026nbsp;\u003c/strong\u003eNot applicable\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability of data and materials:\u0026nbsp;\u003c/strong\u003eNot applicable\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u003c/strong\u003e: The authors declare no conflicts of interest.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding:\u0026nbsp;\u003c/strong\u003eThe work was supported by the Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES) [grant number 001] and the \u003cem\u003eFundação de Amparo à Pesquisa do Estado de Minas Gerais\u003c/em\u003e (Fapemig).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors' contributions:\u003c/strong\u003e Authors PCC, TMS, and RMC conceptualized the study, conducted the statistical analyses, interpreted the data, and drafted and revised the manuscript. Authors MMS, EGM, and TCS contributed to data interpretation and critically reviewed the manuscript for important intellectual content. All authors approved the final version to be published.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgment:\u003c/strong\u003e The authors would like to thank the Federal University of Juiz de Fora, the State University of Rio de Janeiro, and the Federal University of Minas Gerais for their institutional and academic support throughout the development of this work. This study was supported by the Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES) [grant number 001] and the Fundação de Amparo à Pesquisa do Estado de Minas Gerais (Fapemig), whose financial support was essential for the completion of this research.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eBull FC, Al-Ansari SS, Biddle S, et al. World Health Organization 2020 guidelines on physical activity and sedentary behaviour. Br J Sports Med. 2020;54(24):1451-1462. doi:10.1136/bjsports-2020-102955\u003c/li\u003e\n\u003cli\u003eWorld Health Organization. Global Action Plan for the Prevention and Control of Noncommunicable Diseases 2013-2020. World Health Organization; 2013.\u003c/li\u003e\n\u003cli\u003eGlobal action plan on physical activity 2018\u0026ndash;2030: more active people for a healthier world. Geneva: World Health Organization; 2018. doi:10.2471/BLT.20.250936\u003c/li\u003e\n\u003cli\u003eWorld Health Organization. WHO Guidelines on Physical Activity and Sedentary Behaviour. Geneva: World Health Organization; 2020.\u003c/li\u003e\n\u003cli\u003ePhysical Activity Guidelines Advisory Committee. Physical Activity Guidelines Advisory Committee Scientific Report. Washington, DC: U.S. Department of Health and Human Services, 2018.\u003c/li\u003e\n\u003cli\u003eSaunders TJ, McIsaac T, Douillette K, et al. Sedentary behaviour and health in adults: an overview of systematic reviews. Appl Physiol Nutr Metab. 2020;45(10 (Suppl. 2)):S197-S217. doi:10.1139/apnm-2020-0272\u003c/li\u003e\n\u003cli\u003ePatterson R, McNamara E, Tainio M, et al. Sedentary behaviour 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. doi:10.1007/s10654-018-0380-1\u003c/li\u003e\n\u003cli\u003eEkelund U, Steene-Johannessen J, Brown WJ, et al. Does physical activity attenuate, or even eliminate, the detrimental association of sitting time with mortality? A harmonised meta-analysis of data from more than 1 million men and women. The Lancet. 2016;388(10051):1302-1310. doi:10.1016/S0140-6736(16)30370-1\u003c/li\u003e\n\u003cli\u003eGuthold R, Stevens GA, Riley LM, Bull FC. Worldwide trends in insufficient physical activity from 2001 to 2016: a pooled analysis of 358 population-based surveys with 1\u0026middot;9 million participants. Lancet Glob Health. 2018;6(10):e1077-e1086. doi:10.1016/S2214-109X(18)30357-7\u003c/li\u003e\n\u003cli\u003eBauman AE, Petersen CB, Blond K, Rangul V, Hardy LL. The Descriptive Epidemiology of Sedentary Behaviour. In: Leitzmann MF, Jochem C, Schmid D, eds. Sedentary Behaviour Epidemiology. Springer Series on Epidemiology and Public Health. Springer International Publishing; 2018:73-106. doi:10.1007/978-3-319-61552-3_4\u003c/li\u003e\n\u003cli\u003eInstituto Brasileiro de Geografia e Estat\u0026iacute;stica (IBGE). Pesquisa Nacional de Sa\u0026uacute;de 2019: percep\u0026ccedil;\u0026atilde;o do estado de sa\u0026uacute;de, estilos de vida, doen\u0026ccedil;as cr\u0026ocirc;nicas e sa\u0026uacute;de bucal: Brasil e grandes regi\u0026otilde;es. IBGE, Coordena\u0026ccedil;\u0026atilde;o de Trabalho e Rendimento. Rio de Janeiro, 2020. 113p.\u003c/li\u003e\n\u003cli\u003eBrasil. Brasil. Minist\u0026eacute;rio da Sa\u0026uacute;de. Secretaria de Vigil\u0026acirc;ncia em Sa\u0026uacute;de. Departamento de An\u0026aacute;lise em Sa\u0026uacute;de e Vigil\u0026acirc;ncia de Doen\u0026ccedil;as N\u0026atilde;o Transmiss\u0026iacute;veis. Vigitel Brasil 2019: vigil\u0026acirc;ncia de fatores de risco e prote\u0026ccedil;\u0026atilde;o para doen\u0026ccedil;as cr\u0026ocirc;nicas por inqu\u0026eacute;rito telef\u0026ocirc;nico: estimativas sobre frequ\u0026ecirc;ncia e distribui\u0026ccedil;\u0026atilde;o sociodemogr\u0026aacute;fica de fatores de risco e prote\u0026ccedil;\u0026atilde;o para doen\u0026ccedil;as cr\u0026ocirc;nicas nas capitais dos 26 estados brasileiros e no Distrito Federal em 2019 [recurso eletr\u0026ocirc;nico] / Minist\u0026eacute;rio da Sa\u0026uacute;de, Secretaria de Vigil\u0026acirc;ncia em Sa\u0026uacute;de, Departamento de An\u0026aacute;lise em Sa\u0026uacute;de e Vigil\u0026acirc;ncia de Doen\u0026ccedil;as n\u0026atilde;o Transmiss\u0026iacute;veis. \u0026ndash; Bras\u0026iacute;lia: Minist\u0026eacute;rio da Sa\u0026uacute;de, 2020. 137p.\u003c/li\u003e\n\u003cli\u003eKoyanagi A, Stubbs B, Vancampfort D. Correlates of low physical activity across 46 low- and middle-income countries: A cross-sectional analysis of community-based data. Prev Med. 2018;106:107-113. doi:10.1016/j.ypmed.2017.10.023\u003c/li\u003e\n\u003cli\u003eWerneck AO, Baldew SS, Miranda JJ, et al. Physical activity and sedentary behavior patterns and sociodemographic correlates in 116,982 adults from six South American countries: the South American physical activity and sedentary behavior network (SAPASEN). Int J Behav Nutr Phys Act. 2019;16(1):68. doi:10.1186/s12966-019-0839-9\u003c/li\u003e\n\u003cli\u003eWerneck AO, Barboza LL, Ara\u0026uacute;jo RHO, et al. Time Trends and Sociodemographic Inequalities in Physical Activity and Sedentary Behaviors Among Brazilian Adults: National Surveys from 2003 to 2019. J Phys Act Health. 2021;18(11):1332-1341. doi:10.1123/jpah.2021-0156\u003c/li\u003e\n\u003cli\u003eWerneck AO, Araujo RH, Aguilar-Farias N, et al. Time trends and inequalities of physical activity domains and sitting time in South America. J Glob Health. 2022;12:04027. doi:10.7189/jogh.12.04027\u003c/li\u003e\n\u003cli\u003eZwolinsky S, McKenna J, Pringle A, et al. Physical Activity and Sedentary Behavior Clustering: Segmentation to Optimize Active Lifestyles. J Phys Act Health. 2016;13(9):921-928. doi:10.1123/jpah.2015-0307\u003c/li\u003e\n\u003cli\u003eOmorou AY, Coste J, Escalon H, Vuillemin A. Patterns of physical activity and sedentary behaviour in the general population in France: cluster analysis with personal and socioeconomic correlates. J Public Health. 2016;38(3):483-492. doi:10.1093/pubmed/fdv080\u003c/li\u003e\n\u003cli\u003eManta SW, Sandreschi PF, Matias TS, Tomicki C, Benedetti TRB. Clustering of Physical Activity and Sedentary Behavior Associated to Risk for Metabolic Syndrome in Older Adults. J Aging Phys Act. 2019;27(6):781-786. doi:10.1123/japa.2018-0300\u003c/li\u003e\n\u003cli\u003eWerneck AO, Silva DR, Malta DC, et al. Changes in the clustering of unhealthy movement behaviors during the COVID-19 quarantine and the association with mental health indicators among Brazilian adults. Transl Behav Med. 2021;11(2):323-331. doi:10.1093/tbm/ibaa095\u003c/li\u003e\n\u003cli\u003eWorld Health Organization. Global Status Report on Physical Activity 2022. Geneva: World Health Organization; 2022.\u003c/li\u003e\n\u003cli\u003eStopa SR, Szwarcwald CL, Oliveira MM de, et al. Pesquisa Nacional de Sa\u0026uacute;de 2019: hist\u0026oacute;rico, m\u0026eacute;todos e perspectivas. Epidemiol E Servi\u0026ccedil;os Sa\u0026uacute;de. 2020;29(5):e2020315. doi:10.1590/s1679-49742020000500004\u003c/li\u003e\n\u003cli\u003eUnited Nations Development Programme, Funda\u0026ccedil;\u0026atilde;o Jo\u0026atilde;o Pinheiro, Instituto de Pesquisa Econ\u0026ocirc;mica Aplicada, eds. Desenvolvimento Humano Nas Macrorregi\u0026otilde;es Brasileiras. Primeira edi\u0026ccedil;\u0026atilde;o. PNUD Brasil; 2016.\u003c/li\u003e\n\u003cli\u003eWHO Expert Committee on Physical Status: the Use and Interpretation of Anthropometry, ed. Physical Status: The Use and Interpretation of Anthropometry: Report of a WHO Expert Committee. World Health Organization; 1995.\u003c/li\u003e\n\u003cli\u003eRoss R, Chaput JP, Giangregorio LM, et al. Canadian 24-Hour Movement Guidelines for Adults aged 18\u0026ndash;64 years and Adults aged 65 years or older: an integration of physical activity, sedentary behaviour, and sleep. Appl Physiol Nutr Metab. 2020;45(10 (Suppl. 2)):S57-S102. doi:10.1139/apnm-2020-0467\u003c/li\u003e\n\u003cli\u003eO\u0026rsquo;Donoghue G, Perchoux C, Mensah K, et al. A systematic review of correlates of sedentary behaviour in adults aged 18\u0026ndash;65 years: a socio-ecological approach. BMC Public Health. 2016;16(1):163. doi:10.1186/s12889-016-2841-3\u003c/li\u003e\n\u003cli\u003ePrince SA, Melvin A, Roberts KC, Butler GP, Thompson W. Sedentary behaviour surveillance in Canada: trends, challenges and lessons learned. Int J Behav Nutr Phys Act. 2020;17(1):34. doi:10.1186/s12966-020-00925-8\u003c/li\u003e\n\u003cli\u003eSilva DR, Collings P, Araujo RHO, Barboza LL, Szwarcwald CL, Werneck AO. Correlates of screen-based behaviors among adults from the 2019 Brazilian National Health Survey. BMC Public Health. 2021;21(1):2289. doi:10.1186/s12889-021-12340-0\u003c/li\u003e\n\u003cli\u003eWorld Health Organization. Noncommunicable Diseases Country Profiles 2018. World Health Organization; 2018.\u003c/li\u003e\n\u003cli\u003ePrince SA, Roberts KC, Melvin A, Butler GP, Thompson W. Gender and education differences in sedentary behaviour in Canada: an analysis of national cross-sectional surveys. BMC Public Health. 2020;20(1):1170. doi:10.1186/s12889-020-09234-y\u003c/li\u003e\n\u003cli\u003eMansoubi M, Pearson N, Biddle SJH, Clemes S. The relationship between sedentary behaviour and physical activity in adults: A systematic review. Prev Med. 2014;69:28-35. doi:10.1016/j.ypmed.2014.08.028\u003c/li\u003e\n\u003cli\u003eJochem C, Leitzmann M. A call for integrating active transportation into physical activity and sedentary behaviour guidelines. Lancet Planet Health. 2023;7(2):e112-e113. doi:10.1016/S2542-5196(23)00001-3\u003c/li\u003e\n\u003cli\u003eCillekens B, Lang M, Van Mechelen W, et al. How does occupational physical activity influence health? An umbrella review of 23 health outcomes across 158 observational studies. Br J Sports Med. 2020;54(24):1474-1481. doi:10.1136/bjsports-2020-102587\u003c/li\u003e\n\u003cli\u003eRiley L, Guthold R, Cowan M, et al. The World Health Organization STEPwise Approach to Noncommunicable Disease Risk-Factor Surveillance: Methods, Challenges, and Opportunities. Am J Public Health. 2016;106(1):74-78. doi:10.2105/AJPH.2015.302962\u003c/li\u003e\n\u003cli\u003eProchaska JJ, Spring B, Nigg CR. Multiple health behavior change research: An introduction and overview. Prev Med. 2008;46(3):181-188. doi:10.1016/j.ypmed.2008.02.001\u003c/li\u003e\n\u003cli\u003eMekary RA, Willett WC, Hu FB, Ding EL. Isotemporal Substitution Paradigm for Physical Activity Epidemiology and Weight Change. Am J Epidemiol. 2009;170(4):519-527. doi:10.1093/aje/kwp163\u003c/li\u003e\n\u003cli\u003eChastin SFM, Palarea-Albaladejo J, Dontje ML, Skelton DA. Combined Effects of Time Spent in Physical Activity, Sedentary Behaviors and Sleep on Obesity and Cardio-Metabolic Health Markers: A Novel Compositional Data Analysis Approach. Devaney J, ed. PLOS ONE. 2015;10(10):e0139984. doi:10.1371/journal.pone.0139984\u003c/li\u003e\n\u003cli\u003eDumuid D, Pedi\u0026scaron;ić Ž, Stanford TE, et al. The compositional isotemporal substitution model: A method for estimating changes in a health outcome for reallocation of time between sleep, physical activity and sedentary behaviour. Stat Methods Med Res. 2019;28(3):846-857. doi:10.1177/0962280217737805\u003c/li\u003e\n\u003cli\u003eChastin SFM, McGregor DE, Biddle SJH, et al. Striking the Right Balance: Evidence to Inform Combined Physical Activity and Sedentary Behavior Recommendations. J Phys Act Health. 2021;18(6):631-637. doi:10.1123/jpah.2020-0635\u003c/li\u003e\n\u003cli\u003eCurtis RG, Dumuid D, Olds T, et al. The Association Between Time-Use Behaviors and Physical and Mental Well-Being in Adults: A Compositional Isotemporal Substitution Analysis. J Phys Act Health. 2020;17(2):197-203. doi:10.1123/jpah.2018-0687\u003c/li\u003e\n\u003cli\u003eDumuid D, Olds T, Wake M, et al. Your best day: An interactive app to translate how time reallocations within a 24-hour day are associated with health measures. Harezlak J, ed. PLOS ONE. 2022;17(9):e0272343. doi:10.1371/journal.pone.0272343\u003c/li\u003e\n\u003c/ol\u003e"},{"header":"Tables","content":"\u003cp\u003eTables 1 to 4 are available in the Supplementary Files section\u003c/p\u003e"},{"header":"Chart 1","content":"\u003cp\u003eChart 1 is available in the Supplementary Files section.\u003c/p\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Cluster, screen time, physical exercise, behavior patterns, health inequities, public health","lastPublishedDoi":"10.21203/rs.3.rs-8139253/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8139253/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e\u003cp\u003eThis study analyzed the co-occurrence of leisure screen time and leisure-time physical activity in Brazilian adults, and their association with socioeconomic and demographic characteristics, nutritional status, and chronic health conditions.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e\u003cp\u003eData from individuals aged\u0026thinsp;\u0026ge;\u0026thinsp;18 years from the 2019 National Health Survey (n\u0026thinsp;=\u0026thinsp;88,531). Cluster analysis identifield the co-occurrence of screen use (TV and cell phone, computer, or tablet [CCT]) during leisure time and physical activity (leisure, commuting, home, and work). Multivariate multinomial logistic regression was used to identify the factors associated with the clusters.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e\u003cp\u003eFour clusters were identified: (1) low screen use during leisure time and active individuals, especially at work (13.0% of the total population); (2) prolonged TV use and predominance inactive individuals (14.9%); (3) prolonged CCT use and active individuals, especially during leisure time (18.0%); (4) low screens use during leisure time and active individuals (54.1%). Compared to Cluster 1, Cluster 2 was more likely among women (OR: 2.78; p\u0026thinsp;\u0026lt;\u0026thinsp;.001), individuals\u0026thinsp;\u0026ge;\u0026thinsp;60 years old (OR: 8.41; p\u0026thinsp;\u0026lt;\u0026thinsp;.001), with obesity (OR: 1.32; p\u0026thinsp;\u0026lt;\u0026thinsp;.05), diabetes, arterial hypertension, and other cardiovascular diseases (OR: 1.78; 1.27; 1.52, respectively; p\u0026thinsp;\u0026lt;\u0026thinsp;.001) were more associated with cluster 2, while those with higher education (OR: 3.22; p\u0026thinsp;\u0026lt;\u0026thinsp;.001) and higher income (OR: 2.11; p\u0026thinsp;\u0026lt;\u0026thinsp;.001) were more associated with cluster 3, as were those aged\u0026thinsp;\u0026lt;\u0026thinsp;35 years.\u003c/p\u003e\u003ch2\u003eConclusion\u003c/h2\u003e\u003cp\u003eFour lifestyle clusters with socioeconomic, demographic, and health differences were identified, offering relevant support for the development of public health policies.\u003c/p\u003e","manuscriptTitle":"Co-occurrence of Leisure Screen Time and Leisure-time Physical Activity Among Brazilians and Associated Socioeconomic, Demographic, and Health Factors","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-12-01 13:42:31","doi":"10.21203/rs.3.rs-8139253/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"791030b3-4888-4613-bc14-7c5d10f7c8db","owner":[],"postedDate":"December 1st, 2025","published":true,"recentEditorialEvents":[{"type":"decision","content":"Rejected","date":"2026-05-06T23:17:05+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-05-04T09:21:28+00:00","index":42,"fulltext":""}],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2026-05-06T23:24:19+00:00","versionOfRecord":[],"versionCreatedAt":"2025-12-01 13:42:31","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-8139253","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-8139253","identity":"rs-8139253","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}