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This systematic review aimed to synthesize evidence of cross-sectional studies on the associations between physical activity, cardiometabolic risk factors and vitamin D concentrations in children and adolescents. The search was performed in PubMed, SciELO, LILACS, Scopus, MEDLINE and SPORTDiscus. Cardiometabolic risk factors included obesity, insulin resistance, systemic arterial hypertension and unfavorable changes in the lipid profile (low levels of high-density lipoprotein, elevated low-density lipoprotein and triglycerides). Cross-sectional design studies published between 2007 and 2019 were included whether they evaluated the relationship between vitamin D and physical activity and/or vitamin D and cardiometabolic risk factors. Fourteen studies were selected, involving 8340 children and adolescents. The main results found were a significant inverse relationship between vitamin D levels and cardiometabolic risk factors. All studies (n = 8) that tested association between physical activity and 25-hydroxyvitamin D (25 (OH) D) concentrations showed a significant and direct relationship between them. In addition, nine out of eleven studies that tested the association between 25 (OH) D and body mass index reported an inverse and significant relationship between 25 (OH) D and obesity. In conclusion, sufficient concentrations of vitamin D are related to a more favorable cardiometabolic profile, and children and adolescents who are obese or insufficiently active have a higher risk of present hypovitaminosis D.

ObjectiveStudy aimed to evaluate the effect and individual responsiveness after 12-weeks of high-intensity interval training (HIIT) and moderate-intensity of continuous training (MICT) on adiponectin, cardiometabolic risk factors and physical tness in overweight adolescents. MethodsParticipated 52 adolescents, both sexes, 11 and 16 years-old: HIIT (n = 13), MICT (n = 15) and control group (CG, n = 24). Body mass, height, waist circumference (WC), fat mass (FM), fat-free mass (FFM), blood pressure, high-density lipoprotein (HDL-c), low-density lipoprotein (LDL-c), triglycerides, insulin, adiponectin, and C-reactive protein (CRP) were evaluated. Body mass index z-score (BMI-z), waist-to-height ratio (WHtR), insulin resistance and insulin sensitivity were calculated. Resting heart rate (HR rest ), peak oxygen consumption (VO 2peak ), right handgrip strength (HGS-right), left handgrip strength (HGS-left) and abdominal resistance (ABD) was evaluated. HIIT session lasted around 35min and MICT of 60min of exercises on stationary bicycle, three times a weekday for 12-weeks. ANOVA, effect size, and prevalence of responders were used for statistical analysis. ResultsHIIT reduced BMI-z, WHtR, LDL-c and CRP, while increased of physical tness. MICT reduced HDL-c, while increased of physical tness. CG reduced FM, HDL-c, and CRP, while increased FFM and HR rest . Frequencies of respondents in HIIT were observed for CRP, VO 2peak , HGS-right and HGS-left.Frequencies of respondents in MICT were observed for CRP and HGS-right. Frequencies of no-respondents in CG were observed for WC, WHtR, CRP, HR rest and ABD. ConclusionInterventions with exercises were effective to adiposity, metabolic health, and physical tness improvements. Individual responses were observed in in ammatory process and physical tness, important changes in overweight adolescent's therapy.

Objective: The aim of this study was to analyze which equation best estimates maximal heart rate (HRmax) for the pediatric population according to body mass. Data source: We performed a meta-analysis (PROSPERO No. CRD42020190196) of cross-sectional studies that aimed to validate or develop HRmax equations and that had children and adolescents as samples. The search was conducted in Scopus, Science Direct, Web of Science, PubMed, and Biblioteca Virtual em Saúde with the descriptors “prediction or equation,” “maximal heart rate,” “maximum heart rate,” “determination of heart rate,” children, and adolescent. The TRIPOD Statement tool was used to assess the methodological quality and the relevant data were extracted for analysis. The meta-analysis was conducted in the Comprehensive Meta-Analysis, adopting p<0.05 and a 95% confidence interval (CI). Data synthesis: In total, 11 studies were selected, of which 3 developed predictive equations, 10 performed external validity of the preexisting models, and 1 incremented values related to equations already developed. The results of the methodological quality analysis showed a moderate rating in most studies. The 164 + (0.270 × HRres) – (0.155 × body mass) + (1.1 × METs) + (0.258 × body fat percent) (r=0.500, 95%CI 0.426–0.567, p<0.001) and 166.7+ (0.46 × HRres) + (1.16 × maturation) (r=0.540, 95%CI 0.313–0.708, p<0.001) equations presented stronger correlations with measured HRmax in nonobese adolescents. The predictive model developed by 208 – (0.7 × age) showed a greater accuracy among the possible models for analysis (SDM=-0.183, 95%CI -0.787 to -0.422, p=0.554). No specific predictive equation was found for obese adolescents. Conclusions: Future research should explore new possibilities for developing predictive equations for this population as a tool to control exercise intensity in the therapeutic management of childhood and adolescent obesity.