Background The present study aimed to analyse the effect of an intervention for increasing physical activity (PA) and reducing sedentary time on adolescents' total sleep time (TST) and to examine whether the effect on adolescents' TST was mediated by the intervention changes on screen‐based sedentary time. Methods This study was a school‐based cluster‐randomized controlled trial conducted with Brazilian adolescents in Grades 7 to 9 (in 2017). Three schools participated in each of the intervention group (IG) and control group (CG). The intervention involved the following three strategies designed to increase PA and reduce sedentary time: (a) teacher training, (b) environmental improvements, and (c) health educational component. TST and screen‐based sedentary time (television use, cell phone use, playing on computer/video game, and computer use that is not for gaming purposes) on weekdays and weekends were assessed using a standardized questionnaire. Generalized linear mixed models were conducted to verify the effect of the intervention on TST. Mediation analyses were used to examine the mediator role of changes in screen‐based sedentary time in the relationship between the intervention and TST. Results Overall, 597 students participated in this study (mean age: 13.0 ± 1.0 years; IG: n = 351; CG: n = 246). The intervention had no effect on TST on weekdays and weekends. Adolescents in the IG were more likely to report ≤2 hr of television viewing per day on weekdays at follow‐up. Mediated effects by screen media use were not significant. Conclusions The school‐based intervention for increasing PA and reducing sedentary time had no effect on adolescents' TST.

The aim of this study was to summarize data on the prevalence of global physical activity (PA) among children and adolescents in Brazil. This systematic review included an electronic search in 8 databases (PubMed, Scopus, Web of Science, LILACS, SPORTDiscus, BIREME, Scielo, and Google Scholar) and a manual search in the references of retrieved studies. Observational studies that assessed global PA among Brazilian children and adolescents were included. A narrative approach toward results was adopted. An initial database search reached 3276 potentially relevant titles (2534 titles after duplicate analysis); 92 (62 different studies) met all inclusion criteria. Studies were carried out between 2001 and 2015; most studies were conducted in Southern and Northeastern Brazil. Three were nationwide surveys (4.8%), but no studies included children aged 0-5 years. Eighteen different instruments for PA measurement were used; only two studies used objectively-measured PA. The most used definition was ≥ 300 minutes/week of moderate-to-vigorous PA. The overall proportion of young people who were physically active ranged from 6.5% to 92.3%. Fourteen studies (22.6%) found a prevalence of ≥ 50%. Three nationwide surveys reported the prevalence of physically active students ranging from 29.0% to 66.0%. A wide variability of PA estimates were found. Important research gaps (e.g., studies with objectively-measured PA, children aged up to 6 years-old and in Northern Brazil) should be considered to develop new research studies in Brazil.

Objective: To determine the prevalence of smoking, as well as its association with sociodemographic factors, alcohol consumption, and stress levels, among industrial workers in Brazil. Methods: This was a nationwide survey, conducted in 24 capitals in Brazil through the application of a pre-tested questionnaire. The response to the question “What is your smoking status?” was the outcome variable. To determine the associations, we performed Poisson regression analyses in which the inputs were blocks of variables: block 1 (age and marital status); block 2 (level of education and gross family income); block 3 (geographic region); and block 4 (alcohol consumption and stress level). All analyses were stratified by gender. Results: The sample consisted of 47,328 workers ≥ 18 years of age, of whom 14,577 (30.8%) were women. The prevalence of smoking was 13.0% (15.2% in men and 7.9% in women). Advancing age, alcohol consumption, and a high stress level were positively associated with smoking. A lower risk of smoking was associated with being married, having a higher level of education, and living in the northeastern region of the country (versus the southern region). Conclusions: The prevalence of smoking was greater in men than in women. Alcohol consumption and high stress levels appear to promote smoking.

No abstract

– This study aimed to summarize existing research examining the prevalence of adequate sleep duration or meeting sleep recommendations among Brazilian children and adolescents. A systematic review was conducted on eight databases. The criteria for inclusion were original research, samples including Brazilian children and adolescents (age 0–18), studies that reported variables regarding sleep duration using both objective or subjective measures, school- or population-based surveys with random sampling, and cross-sectional or cohort studies. Of the 54 articles included, only 27 used data from different studies. Most of the studies were conducted in the southern region (n = 17) with only two studies having a nationally representative sample. Two studies included children below age five, and the age group most investigated was adolescents (>10 years old). A majority of the studies used a self-reported questionnaire and showed variability in the cutoff point applied for adequate sleep duration with the most reported being ≥8 hours. The proportion of adolescents who met recommendations or had adequate sleep duration ranged from 15% to 89%. Among children, the prevalence ranged from 17% to 95%. The least variability was observed among toddlers, ranging from 93% to 95%. No consistent gender differences were observed among the gender-stratified groups of children and adolescents across the studies. It can be observed that, because of the wide range of results and the lack of data with national representativeness, the prevalence of sufficient sleep among young people aged below 18 remains undefined in Brazil.

Physical activity is influenced by an interplay of time and setting, thus, the role of psychosocial factors on behavior is expected to vary across the day. This cross-sectional study identified the psychosocial correlates of time spent in physical activity during in-school (i.e., 08:00–11:59) and out-of-school (i.e., 12:00–22:00) time segments. The sample consisted of adolescents (7th to 9th grade) of two public schools in Florianópolis, Brazil. Accelerometer measured light-intensity (LPA) and moderate-to-vigorous-intensity physical activity (MVPA) were estimated. Psychosocial correlates of physical activity (i.e., outcome expectations, attitude, self-efficacy, support from parents, and support from peers) were self-reported. Linear mixed models were applied and interaction effects of gender and age were also analyzed. From a total of 194 participants, 138 and 112 provided in-school and out-of-school valid data, respectively. Outcome expectations were associated with out-of-school MVPA (β = 0.18; p = .006). Age interaction effects were observed for the associations between outcome expectations and out-of-school LPA (β = 0.21; p = .009) and between self-efficacy and out-of-school MVPA (β = 0.19; p = .009). In conclusion, psychosocial correlates were positively associated with out-of-school physical activity and the effects varied according to age. No psychosocial correlates were identified for LPA and MVPA performed during schooltime.

Purpose: This study analyzed the content validity and reliability of the Questionnaire for Screen Time of Adolescents (QueST). Methods: QueST measures screen time across five constructs: studying, working/internship-related activities, watching videos, playing video games, and using social media/chat applications. The content validity, including a pretest, was carried out by experts and adolescents. For reliability analysis, QueST was applied and reapplied after one week in a sample of 104 adolescents (16.3 ± 1.02 years; 66.3% girls). Results: The Content Validity Index for Scales indicated 94% and 98% of overall clarity and representativeness, respectively. The QueST was considered comprehensible and clear by adolescents. The intraclass correlation coefficients ranged from 0.41 (95% CI 0.24, 0.56) for videos to 0.76 (95% CI 0.66, 0.83) for social media/chat applications on a weekday, and from 0.24 (95% CI 0.04; 0.41) for videos to 0.67 (95% CI 0.54; 0.77) for social media/chat applications on weekends. Conclusions: The QueST has demonstrated satisfactory content validity; however, measuring the time watching videos during free-living is a challenge for researchers. In general, the QueST is recommended to measure different screen time constructs.

Background: Income is an important determinant of physical activity (PA) when analyzed in its different domains. Sociodemographic characteristics such as sex, age, education, and marital status reveal distinct population profiles when PA domains are analyzed in isolation. This study aimed to describe clusters of PA in domains within income inequalities and to investigate the associated sociodemographic characteristics of Brazilian adults. Methods: A secondary analysis of the National Health Survey was performed (N = 50,176). PA, sociodemographic characteristics, and family income were investigated. Low- (n = 9504) and high-income adults (n = 6330) were analyzed. Two-step cluster and Rao–Scott chi-square tests were employed. Results: High-income adults accumulated 1.06 times more PA in leisure time compared with low-income adults. Of the 3 clusters observed, the inactive cluster was more prevalent (low-income group: 65.9%; 95% confidence interval [CI], 64.1–67.5; high-income group: 84.5%; 95% CI, 82.9–86.0). Work/leisure activities (21.2%; 95% CI, 19.8–22.8) and commuting/household activities (12.9%; 95% CI, 11.8–14.1) characterized low-income adults. Work/household activities (10.9%; 95% CI, 9.6–12.3) and commuting/leisure activities (4.6%, 95% CI, 3.9–5.4) characterized high-income adults. Sex (P < .001), age (P < .001), and marital status (P = .0023) were associated with low-income clusters. Conclusion: PA clustering differs within income inequalities. PA in leisure differentiates the opportunities in low- and high-income groups, but it is representative of a very small portion of the wealth.