Resumo Fundamento A frequência cardíaca em repouso (FCR) pode ser uma ferramenta útil de triagem para o risco cardiovascular. Porém, os pontos de corte para FCR nunca foram descritos em populações jovens. Objetivo Estabelecer os pontos de corte para FCR em adolescentes brasileiros e analisar se há associação entre pontos de corte e fatores de risco cardiovascular. Métodos A amostra foi composta por 6.794 adolescentes (de 10 a 19 anos). A pressão arterial e a FCR foram avaliadas por dispositivo oscilométrico. Também foram avaliados o índice de massa corporal e a circunferência da cintura. Foi adotada a curva ROC para analisar a sensibilidade e especificidade, e as associações de FCR elevada com os fatores de risco cardiovascular foram analisadas por regressão logística binária. Foi considerado estatisticamente significante um valor de p < 0,05 para todas as análises. Resultados Os valores médios da FCR eram mais altos entre os participantes de 10 a 14 anos do naqueles de 15 a 19 anos, em meninos (p < 0,001) e meninas (< 0,001). Os pontos de corte de FCR propostos para detecção de fatores de risco cardiovascular foram significativos para meninos de 10 a 14 (> 92 bpm) e de 15 a 19 anos (> 82 bpm) e para meninas de 15 a 19 anos (> 82 bpm) (p < 0,05 para todos), enquanto nenhum ponto de corte foi identificado para as meninas de 10 a 14 anos (p > 0,05). Os pontos de corte propostos para a FCR foram associados com obesidade abdominal, sobrepeso e pressão arterial elevada em meninos e meninas. Os pontos de corte da FCR foram associados ao conjunto de fatores de risco cardiovascular em adolescentes de 15 a 19 anos. Conclusões Os pontes de corte propostos para a FCR foram associados com os fatores de risco cardiovascular em adolescentes.

Abstract Background Resting heart rate (RHR) may be a useful screening tool for cardiovascular risk. However, RHR cutoff points, an interesting clinical approach, have never been described in young populations. Objective To establish RHR cutoff points in Brazilian adolescents and to analyze whether cutoff points are associated with cardiovascular risk factors. Methods The sample was composed of 6,794 adolescents (10 to 19 years old). Blood pressure and RHR were assessed by oscillometric device. Body mass index and waist circumference were also assessed. Receiver operating characteristic curve was adopted to analyze the sensitivity and specificity, and associations of high RHR with cardiovascular risk factors were analyzed by binary logistic regression. A p value < 0.05 was considered statistically significant for all the analyses. Results Mean RHR values were higher among participants ages 10 to 14 years than 15 to 19 years, for boys (p < 0.001) and girls (< 0.001). The proposed RHR cutoff points for cardiovascular risk factors detection were significant for boys ages 10 to 14 (> 92 bpm) and 15 to 19 years (> 82 bpm), as well as for girls ages 15 to 19 years (> 82 bpm) (p < 0.05 for all), whereas no cutoff point was identified for girls ages 10 to 14 years (p > 0.05). Proposed RHR cutoff points were associated with abdominal obesity, overweight, and high blood pressure in boys in girls. RHR cutoff points were associated with the cluster of cardiovascular risk factors in adolescents ages 15 to 19 years. Conclusion The proposed RHR cutoff points were associated with cardiovascular risk factors in adolescents.

Abstract Aim: to analyze the pattern and trends of use of screen-based devices and associated factors from two surveys conducted on public high school students in Caruaru-PE. Methods: two representative school-based cross-sectional surveys conducted in 2007 (n=600) and 2012 (n=715) on high school students (15-20 years old). The time of exposure to television (TV) and computer/videogames PC/VG was obtained through a validated questionnaire, and ≥3 hours/day was considered as being excessive exposure. The independent variables were socioeconomic status, school related, and physical activity. Crude and adjusted binary logistic regression were employed to examine the factors associated with screen time. The statistical significance was set at p<0.05. Results: There was a significant reduction in TV time on weekdays and total weekly, but no change in the prevalence of excessive exposure. The proportion of exposure to PC/VG of ≥3 hours/day increased 182.5% on weekdays and 69.5% on weekends (p <0.05). In 2007, being physically active was the only protection factor for excessive exposure to total screen time. In 2012, girls presented less chance of excessive exposure to all screen-based devices and total screen time. Other protective factors were studying at night and being physically active (PC/VG time), while residing in an urban area [OR 5.03(2.77-7.41)] and having higher family income [OR 1.55(1.04-2.30)] were risk factors. Conclusion: Significant and important changes in the time trends and pattern of use PC/VG were observed during the interval of 5 years. This rapid increase could be associated with increased family income and improved access to these devices, driven by technological developments.

Abstract Aims: To analyze the average and individual responses of sleep quality and daytime sleepiness in adolescents after four weeks of strength training. Methods: 19 adolescents with sleep problems recruited in the Federal Institute of Pernambuco, were subject to anthropometric evaluations as well as those for body composition assessment, a 1 repetition maximum test, the sleep parameters (Pittsburgh Sleep Quality Index-PSQI and Epworth Sleepiness Scale-ESS) and were submitted to four weeks of strength-training, performed alternately by segment, two sessions per week, according to recommendations for this population. Results: A decrease in the average PSQI score was observed (10.3±3.3 vs 8.8±4.0; p=0.006), but not in ESS (p>0.05), after intervention. The individual analyses demonstrated that ~63% of adolescents experienced reductions ≥ 3 points in the PSQI and ~58% of them experienced reductions ≥ 3 points in the measure of daytime sleepiness. The prevalence of poor sleep quality and daytime sleepiness reduced from 84.2% to 68.4% and from 52.6% to 31.6%, respectively. The comparisons of high and low responders to exercise training show that adolescents who reduced ≥3 points in the score of a least one sleep parameter presented lower weight, fat mass, and fat percentage (p<0.05). Conclusion: A short-term strength-training program is able to improve global sleep quality, but not daytime sleepiness in adolescents. Furthermore, the changes after training are highly heterogeneous. Further studies are required to better understand the effects of strength training on sleep parameters of adolescents.