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This article aims to identify the association of sociodemographic factors and lifestyle behaviours with bullying perpetration and victimization among high school students. The adolescents (n=852) answered a questionnaire about bullying (victims and perpetrators), sociodemographic factors (sex, age, maternal education, and participant's work status), tobacco use, alcohol use, illicit drug experimentation, physical activity, screen time, and sleep duration. Multilevel logistic regression models were performed. Older adolescents were less likely to be victims of bullying. Females were less likely to be perpetrators or victims of bullying. Adolescents who were working were more likely to be involved in bullying in both forms. Participation in non-sport activities and alcohol consumption were associated with higher odds of bullying victimization. We have identified specific populational subgroups that are more susceptible to being victims and/or perpetrators of bullying, which could support tailor-specific interventions to prevent bullying.

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Abstract This article aims to identify the association of sociodemographic factors and lifestyle behaviours with bullying perpetration and victimization among high school students. The adolescents (n=852) answered a questionnaire about bullying (victims and perpetrators), sociodemographic factors (sex, age, maternal education, and participant's work status), tobacco use, alcohol use, illicit drug experimentation, physical activity, screen time, and sleep duration. Multilevel logistic regression models were performed. Older adolescents were less likely to be victims of bullying. Females were less likely to be perpetrators or victims of bullying. Adolescents who were working were more likely to be involved in bullying in both forms. Participation in non-sport activities and alcohol consumption were associated with higher odds of bullying victimization. We have identified specific populational subgroups that are more susceptible to being victims and/or perpetrators of bullying, which could support tailor-specific interventions to prevent bullying.

Resumo O objetivo deste artigo é identificar a associação de fatores sociodemográficos e comportamentos de estilo de vida com a perpetração do bullying e da vitimização entre os alunos do ensino médio. Os adolescentes (n=852) responderam a um questionário sobre bullying (vítimas e perpetradores), fatores sociodemográficos (sexo, idade, educação materna e status profissional dos participantes), uso de tabaco, uso de álcool, experimentação de drogas ilícitas, atividade física, tempo de tela e duração do sono. Modelos de regressão logística multinível foram realizados. Os adolescentes mais velhos eram menos propensos a serem vítimas de bullying. As mulheres tinham menos probabilidade de serem perpetradoras ou vítimas de bullying. Os adolescentes que estavam trabalhando tinham maior probabilidade de estarem envolvidos em bullying em ambas as formas. A participação em atividades não esportivas e o consumo de álcool estavam associados a maiores probabilidades de vitimização por bullying. Identificamos subgrupos populacionais específicos que são mais suscetíveis a serem vítimas e/ou perpetradores de bullying, o que poderia apoiar intervenções específicas sob medida para evitar o bullying.

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This study is an investigation of the associations of time spent in different screen time activities with bullying among Brazilian adolescents. In this cross-sectional study, adolescents answered questions related to bullying in the past 30 days and reported the weekly volume of screen time spent studying, working, watching videos, playing video games, and using social media applications. Multilevel logistic regression models were used. Our results indicate that higher social media use was associated with higher odds of bullying victimization among males but not females. Excessive use of screen time for work and social media purposes was associated with a higher likelihood of bullying victimization.

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Objectives: Little is known about the association between specific types of screen time and adolescents' substance use. Thus, this study aimed to investigate the associations between screen time for studying, working, watching movies, playing games, and using social media and frequency of alcohol and tobacco use. Methods: In this cross-sectional study, Brazilian adolescents answered survey questions related to frequency of tobacco and alcohol consumption, and reported their daily volume of five types of screen time. Multilevel ordered logistic regression models were performed. Results: Each 1-hour increase in ST for studying was associated with 26% lower odds of smoking (OR = 0.74; 95% CI: 0.61-0.90) and 17% lower odds of drinking alcohol (OR = 0.83; 95% CI: 0.76-0.91) in the past 30 days. The increase of 1 hour of social media use was associated with 10% greater odds of smoking (OR = 1.10; 95% CI: 1.02-1.18) and a 13% greater chance of consuming alcohol (OR = 1.13; 95% CI: 1.08-1.18) in the past 30 days. Conclusion: The association between screen time and substance use appears to be type-specific. Future longitudinal research is needed to explore causal relationships.

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Although there is consistent evidence of the beneficial effects of leisure physical activity (PA) on mental health, the role of PA in the domestic, transport, and occupational domains is inconclusive. This study aimed to analyze the relationship between domain-specific PA and depressive symptoms and examine whether the association is moderated by age in a representative sample of the Brazilian population. Cross-sectional data of 89,923 (52.4% female) individuals aged ≥15 years were analyzed. Depressive symptoms were assessed using the Patient Health Questionnaire-9. Physical activity (min/week) performed in different domains (domestic, transport, occupation, and leisure) was self-reported. Generalized additive models with penalized splines were used to explore associations. Lower leisure-time PA and higher levels of PA in the domestic, occupational, and transport domains at distinct PA thresholds were associated with higher depressive symptoms. Leisure PA, even at lower levels, was associated with lower depressive symptoms compared to no PA at all. The increase in depressive symptoms as a function of occupational PA was observed only at PA levels higher than 40 h/week. Although non-leisure PA levels were related to higher depressive symptoms among the total sample, higher domestic and transport PA levels were related to lower depressive symptoms among older adults. This study provides insights into the non-linearity and age-group dependence of the relationship between domain-specific PA and depressive symptoms in a middle-income country. The evidence suggests that care should be taken on recommending PA regardless of context or domain.

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PURPOSE: This study analyzed day-to-day estimates of bidirectional associations between sleep parameters and intensity-specific physical activity and assessed whether the timing of physical activity influences these relationships. METHODS: The sample was comprised of 651 high school students (51.2% female, 16.33 [1.0] y old) from southern Brazil. Physical activity and sleep were measured using accelerometers. Multilevel models were applied to test associations of nocturnal total sleep time, onset, and efficiency with moderate to vigorous and light (LPA) physical activity. RESULTS: Higher engagement in moderate to vigorous physical activity and LPA was associated with increased total sleep time, and this effect was greater when physical activity was performed in the morning. Morning and evening LPA were associated with increased sleep efficiency and reduced total sleep time, respectively. Practice of LPA in the morning leads to early sleep onset, whereas evening LPA was associated with later onset. Higher total sleep time and later sleep onset were associated with lower moderate to vigorous physical activity and LPA on the following day. However, higher sleep efficiency was associated with increased LPA. CONCLUSION: The relationship between sleep parameters and physical activity is bidirectional and dependent on physical activity intensity and timing.

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abstract This study aimed (1) to verify the relationship between the JIF, Eigenfactor, Citescore and SJR metrics of Physical Education journals throughout Qualis 2013-2016; and (2) to analyze their associations with the field-concerned Web-Qualis grades (based on the 2013-2016 quadrennium criteria). WebQualis grades, which refers to 2015-2018 data, were acquired by accessing officially available data. The scientometrics Journal Impact Factor (JIF), Citescore, Eigenfactor, and Scimago Journal Rank (SJR) for the years 2013 to 2016 and 2018 were extracted from official Clarivate Analytics and Elsevier sources. Approximately half of Physical Education journals included in the 2018 WebQualis database were indexed in Web of Science Collection and Scopus databases. The main results demonstrated high and stable appeared bivariate correlations between all scientometrics from 2013 to 2016. Both JIF, Eigenfactor, Citescore and SJR were associated with WebQualis grades. However, better adjustment of parameters was observed in the model that included SJR as the WebQualis predictor. The field normalized SJR appeared to better predict the WebQualis defined by the 2013-2016 Qualis criteria, which includes the adherence indicator. If researchers consider that distinct research areas are not equally weighted and require specific evaluation, as the authors suggest, use the normalized metrics to classify journals in Brazilian scenarium.

resumo O objetivo deste estudo foi verificar a relação entre as métricas JIF, Eigenfactor, Citescore e SJR de periódicos de Educação Física ao longo do Qualis 2013-2016; e (2) analisar suas associações com as classificações do Web-Qualis (critério de classificação do quadriênio 2013-2016). As classificações do WebQualis, referentes aos dados de 2015-2018, foram adquiridas acessando fontes de dados oficiais. As métricas Journal Impact Factor (JIF), Citescore, Eigenfactor e Scimago Journal Rank (SJR) para os anos de 2013 a 2016 e 2018 foram extraídas de fontes oficiais da Clarivate Analytics e Elsevier. Aproximadamente metade dos periódicos de Educação Física incluídos no banco de dados WebQualis de 2018 foram indexados nos bancos de dados Web of Science Collection e Scopus. Os principais resultados demonstram altas e estáveis correlações bivariadas entre todas as métricas ​​de 2013 a 2016. Ambos o JIF, o Eigenfactor, o Citigenore e o SJR foram associados à classificação do WebQualis. No entanto, melhores parâmetros de ajuste foram observados no modelo que incluiu a SJR como preditor do WebQualis. O SJR normalizado por área pareceu melhor predizer o WebQualis definido pelo critério Qualis de 2013 de 2016, que inclui o indicador de aderência. Se os pesquisadores considerarem que áreas distintas de pesquisa não são igualmente ponderadas e requerem avaliação específica, como sugestão dos autores, a utilização das métricas normalizadas para classificar os periódicos no cenário brasileiro.

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OBJECTIVE: To examine the socioeconomic indicators associated with engagement in physical activity (PA) in the leisure-time, transportation, domestic and occupational domains, in Brazilian adults. METHODS: Cross-sectional study with secondary data from the National Health Survey (PNS), conducted in 2019. The factors associated with engagement in PA were analyzed using logistic regression. RESULTS: The study involved 88,500 Brazilian adults with mean age of 45 ± 17.5 years old. Longer working hours [odds ratio (OR) = 0.74; 95%CI 0.66;0.82; > 40h vs. ≥ 20h] and female sex (OR = 0.67; 95%CI 0.63;0.71) were associated with lower chances of engaging in leisure-time PA. Higher income (OR = 3.20; 95%CI 2.79;3.67; > 5 vs. ≥ minimum wage) and education level (OR = 3.01; 95%CI 2.74;3.32 - complete higher education vs. incomplete elementary school) were positively associated with leisure-time PA. CONCLUSION: Socioeconomic correlates were strongly related to engagement in PA in Brazilian adults, suggesting a pattern of inequity marked by the need for survival, which is socially reproduced.

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BACKGROUND: Little is known about how the interplay among health-related behaviors impacts self-rated health (SRH). We examined the clustering of physical activity (PA), sleep, diet, and specific screen-based device use, and the associations between the emergent clusters and SRH among Brazilian adolescents. METHOD: The data used in this cross-sectional study were from the baseline of the Movimente Program. Self-reported data were analyzed. SRH was recorded as a 5-point scale (from poor to excellent). Daily duration of exposure to the computer, the television, the cell phone, and games; PA; sleep; and weekly consumption of fruits and vegetables and ultra-processed foods were included in a Two-Step cluster analysis. Multilevel ordered logistic regressions assessed the associations between the clusters and SRH. RESULTS: The data of 750 students (girls: 52.8%, 13.1 ± 1.0 years) were analyzed. Good SRH was more prevalent (52.8%). Three clusters were identified: the Phubbers (50.53%; characterized by the longest cell phone use duration, shortest gaming and computer use, lowest PA levels, and low consumption of fruits and vegetables), the Gamers (22.80%; longest gaming and computer use duration, PA < sample average, highest intake of ultra-processed foods), and a Healthier cluster (26.67%; physically active, use of all screen-based devices < sample average, and healthier dietary patterns). For both Gamers (-0.85; 95% CI -1.24, -0.46) and Phubbers (-0.71; 95% CI -1.04, -0.38), it was found a decrease in the log-odds of being in a higher SRH category compared with the Healthier cluster. CONCLUSION: Specific clusters represent increased health-related risk. Assuming the interdependence of health-related behaviors is indispensable for accurately managing health promotion actions for distinguishable groups.

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BACKGROUND: Physical activity, sleep, and sedentary behaviors compose 24-h movement behaviors and have been independently associated with depressive symptoms. However, it is not clear whether it is the movement behavior itself or other contextual factors that are related to depressive symptoms. The objective of the present study was to examine the associations between self-reported and accelerometer-measured movement behaviors and depressive symptoms in adolescents. METHODS: Cross-sectional data from 610 adolescents (14-18 years old) were used. Adolescents answered questions from the Center for Epidemiological Studies Depression scale and reported time spent watching videos, playing videogames, using social media, time spent in various physical activities, and daytime sleepiness. Wrist-worn accelerometers were used to measure sleep duration, sleep efficiency, sedentary time, and physical activity. Mixed-effects logistic regressions were used. RESULTS: Almost half of the adolescents (48%) were classified as being at high risk for depression (score ≥20). No significant associations were found between depressive symptoms and accelerometer-measured movement behaviors, self-reported non-sport physical activity, watching videos, and playing videogames. However, higher levels of self-reported total physical activity (odd ratio (OR) = 0.92, 95% confidence interval (95%CI): 0.86-0.98) and volume of sports (OR = 0.88, 95%CI: 0.79-0.97), in minutes, were associated with a lower risk of depression, while using social media for either 2.0-3.9 h/day (OR = 1.77, 95%CI: 1.58-2.70) or >3.9 h/day (OR = 1.67, 95%CI: 1.10-2.54), as well as higher levels of daytime sleepiness (OR = 1.17, 95%CI: 1.12-1.22), were associated with a higher risk of depression. CONCLUSION: What adolescents do when they are active or sedentary may be more important than the time spent in the movement behaviors because it relates to depressive symptoms. Targeting daytime sleepiness, promoting sports, and limiting social media use may benefit adolescents.

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