Atividade física, duração do sono, tempo de tela e o desenvolvimento de risco cardiometabólico em crianças e adolescentes.

Abstract

Introduction: The increased prevalence of cardiometabolic risk in the child and youth population, including young Brazilians, raises important concerns at the public health level due to its associations with the development of cardiovascular diseases during adolescence and throughout adulthood. In this sense, a complexity of factors is observed, including lifestyle changes. Given the presence of risk behaviors shown at an early age, such as physical inactivity, inadequate sleep duration, sedentary behavior, and increased body adiposity, it is essential to monitor this population's health status and lifestyle habits. This becomes relevant, especially since the role of physical activity, sleep duration, and screen time in the development of cardiometabolic risk has not been well established. Aim: To verify the role of lifestyle aspects in the development of cardiometabolic risk in children and adolescents. Article I: Aim: To verify the prevalence and the integrated role of lifestyle habits in cardiometabolic risk factors according to sex in adolescents. Methods: Cross-sectional study developed with 1,502 adolescents, aged 10 to 17 years. Lifestyle habits included physical activity, screen time, and sleep duration evaluated through a questionnaire. Cardiometabolic risk score (CMRS) was calculated by summing z-scores, divided by six. For statistical analyses, multivariable binary and multinomial logistic regression models were used. Results: 80.7% of the boys classified with adverse CMRS presented physical inactivity, compared to normal CMRS. In girls, 42.6% showed inadequate sleep compared to normal CMRS. Boys classified as inactive showed higher odds for obesity, as well as altered triglycerides, and systolic blood pressure, risk for cardiorespiratory fitness (CRF), high waist circumference, and CMRS, compared to the active. A prolonged screen time increased the odds for altered glucose and decreased the odds for altered triglycerides. In girls, inadequate sleep duration presented higher odds for overweight, obesity, risk for CRF, and high CMRS, compared to adequate sleep. Conclusions: Physical activity for boys and sleep duration for girls are important to maintain healthy metabolic health among youth. Article II: Purpose: To evaluate the simultaneous moderating role of sleep duration and screen time in the relationship between waist circumference (WC) and clustered cardiometabolic risk score (cMetS) according to children and adolescents' physical activity. Methods: A cross-sectional study was conducted on 3,072 children and adolescents (aged 6-17 years, 57.5% girls). Physical activity, sleep duration, and screen time were assessed through a self-report questionnaire. The cMetS was determined by averaging the z-scores of risk factors and dividing it by four. Moderation analyses were tested through multiple linear regression models. Results: Among physically active individuals, sleep duration (p=0.85) and screen time (p=0.96) had no influence on the relationship between WC and cMetS. However, a positive interaction between WC x screen time and cMetS (p=0.04) was observed for physically inactive participants. Concerning sleep duration, there was no interaction with WC. Participants who spent 60 min of screen time presented lower cMetS, even presenting high WC, compared to the higher tertiles of screen time (180 and 360 minutes). However, although the interaction between sleep duration and WC was not significant, it was observed that the lowest tertile of sleep duration (482 min) combined with 60 minutes of screen time presented lower cMetS, even with the presence of high WC. Conclusions: Our findings encourage compliance with physical activity guidelines associated with the adoption of adequate screen time to minimize the influence of waist circumference on cMetS. Article III: Aims: 1) to investigate the longitudinal relationships between ST, sleep duration, PA, body mass index (BMI), and cardiometabolic risk score (cMetS) in children and adolescents; and 2) to verify scores and prevalence of cMetS risk zones at baseline and follow-up. Methods: This observational longitudinal study included 331 children and adolescents (aged six to 17 years; girls=191) from schools in a southern city in Brazil. ST, sleep duration, and PA were evaluated by a self-reported questionnaire. BMI was evaluated using the BMI z-scores (Z_BMI). The cMetS was determined by summing sex- and age-specific z-scores of total cholesterol/high-density lipoprotein cholesterol (HDL-C) ratio, triglycerides, glucose, and systolic blood pressure and dividing it by four. A two-wave cross-lagged model was implemented. Results: ST, sleep duration, and PA were not associated with cMetS after 2- years. However, it was observed that ST at baseline was associated with sleep duration at follow-up (B=-0.074; 95%IC=-0.130; -0.012), while Z_BMI from baseline associated with cMetS of follow-up (B=0.154; 95%CI=0.083;0.226). The reciprocal model of relationships indicated that the variance of ST, sleep time, PA, Z_BMI, and cMetS explained approximately 9%, 14%, 10%, 67% and 22%, respectively, of the model. Individual change scores and prevalence indicated that cMetS had individual changes from 2014 to 2016. Conclusion: Sleep duration, ST and PA were not associated with cMetS after 2 years. ST showed an inverse association with sleep duration, and Z_BMI was positively associated with cMetS after a 2-year follow-up. Finally, the prevalence of no clustering of risk factors increased after two years. These findings suggest the need to promote healthy lifestyle habits from childhood and considering individual factors that can influence cardiometabolic health. Article IV: Aim: This study aims to verify the moderation role of changes in the levels of PA in the association between ST at baseline with cardiometabolic risk score (cMetS) at 2-years follow-up in children and adolescents. Methods: Observational longitudinal study with children and adolescents of 6 to 17 years old, both sexes (girls n= 141, boys n= 192), from public and private schools. Participants were evaluated in 2014 and 2016. The evaluation of the ‘levels of PA’ (both periods: 2014 and 2016) and ST behavior at baseline was applied through a self-reported questionnaire. Delta changes in PA (ΔPA) were calculated through subtraction of PA at follow-up and PA at baseline. The cMetS at baseline and follow-up was obtained by summing z-scores of each cardiometabolic factor risk (waist circumference, systolic blood pressure, triglycerides, TC/HDL-c ratio, and fasting glucose) and dividing it by 5. Moderation models were tested using multiple linear regression. Results: A positive interaction term between ΔPA and screen time at baseline in the association with cMetS at follow-up (p = 0.029) was observed, indicating that the association between ST at baseline and cMetS at follow-up was significantly different across different values of ΔPA. From ΔPA ≥ 36.92 minutes per week, ST at baseline and cMetS at 2-years follow-up exhibited a negative statistically significant relationship (p = 0.050), indicating that even though participants presented higher amounts of ST at baseline, the cardiometabolic profile at follow-up tends to be healthy when there is a change in PA from baseline to follow-up higher than 36.92 minutes per week. Conclusions: The increases in PA after 2-years follow-up moderate significantly the negative association of ST with cardiometabolic diseases by improving metabolic syndrome markers in schoolchildren (children and adolescents). Finals conclusions: It is important to explore the development of cardiometabolic risk factors in multiple lifestyle behaviors, such as physical activity sleep duration, and screen time, considering the simultaneous influence between them. From that, public health and education strategies should encourage the child and youth population to adopt healthy lifestyle habits, meeting recommendations for physical activity, sedentary behavior, and sleep duration for the prevention of cardiometabolic diseases.