The aim of this study was to evaluate the mediation role of muscle quantity in the relationship between physical fitness and cardiometabolic risk factors (CMRF) in adolescents. This cross-sectional study conducted with 120 adolescents of both sexes, aged between 10 and 17 years. Body mass, height, fat mass (FM), lean mass, blood pressure, high-density lipoprotein, low-density lipoprotein, triglycerides, glucose, insulin, cardiorespiratory fitness (CRF) and 1 repetition maximum strength (1-RM) with evaluation of the leg press 45° (RM-leg), bench press (RM-bench) and arm curl (RM-arm). Body mass index z-score, appendicular skeletal muscle mass, appendicular skeletal muscle mass index, lean mass index (LMI), muscle-to-fat ratio (MFR), age at peak height velocity, and CMRF z-score were calculated. The direct relation between FM and CMRF was mediated by the LMI (26%) and inverse relation between CRF and CMRF was mediated by the LMI (26%). For girls, the direct relation between FM and CMRF was mediated by the LMI (32%); the inverse relation between CRF, RM-leg, RM-arm and CMRF was mediated by the LMI (32%, 33%, and 32%, respective). For boys, the indirect effect was not significant, indicating that LMI is not a mediator in the relation between FM, CRF, 1-RM with CMRF. The direct relation between RM-leg and CMRF was mediated by the MRF (16%). This finding evidenced the importance of promoting a healthy lifestyle to improve physical fitness levels and the quantity of muscle mass in adolescents.

The aim of this study was to analyze the relationship between body adiposity and physical fitness with performance in the Supine-to-Stand test (STS-test) in sedentary adolescents. Sixty-two adolescents, of both sexes, between 10 and 16 years old, participated in the study. Body mass (BM), height, waist circumference (WC), fat mass (FM), fat-free mass (FFM), right and left handgrip strength (HGS-right, HGS-left), abdominal resistance (ABDO), flexibility (FLEX), and cardiorespiratory fitness (VO_2peak) were measured. Body mass index (BMI), z-score BMI (BMI-z), tri-ponderal mass index (TMI) and waist-to-height ratio (WHtR) were calculated. The STS-test was applied to evaluate the STS-MC by the movement patterns in the execution of the test. The STS-time in seconds (s) was categorized into terciles: fast (FG < 2.0 s), intermediate (IG = 2.0-2.6 s) and slow (SG > 2.6 s). One-way ANOVA, Chi-square, Spearman's correlation coefficient as well as non-parametric tests were used, with significance p ≤ 0.05. The SG presented higher BMI, BMI-z, TMI, WHtR, FM, %FM, as well as lower averages for %FFM, HGS-right, HGS-left, FLEX, ABDO, VO_2peak, VO_2peak relative to BM (VO_2peakBM) in relation to GF. The BMI, BMI-z, TMI, WC, WHtR and FM showed moderate and direct correlations with STS-time and inverse with STS-MC (p < 0.01). HGS-right, HGS-left, ABDO, and VO_2peakBM showed moderate and an inverse correlation with STS-time (p < 0.05). The VO_2peakBM was moderate and with direct correlations to STS-MC (p < 0.01). It is concluded that excess fat and low physical fitness hamper STS-test performance. Therefore, the STS-test can be used for screening students to assess MC.

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.

Introduction: Obesity in adolescents has increased worldwide, and is generally associated with poor eating habits and physical inactivity. Objective: To compare absolute and relative muscle strength with body mass (BM), fat-free mass (FFM) and localized FFM of upper and lower limbs among obese and non-obese adolescents. Methods: BM, height and body mass index (BMI) were verified in 39 male adolescents (aged 13-17 years). Body composition was measured by dual-energy X-ray absorptiometry (DXA) and maximal strength of upper and lower limbs was estimated by a one-repetition maximum (1RM) test. Participants were divided into three groups: eutrophic (normal weight) (n=11), overweight (n=14), and obese (n=14). One-way ANOVA was used to compare the variables, followed by a Bonferroni post-hoc test for multiple comparisons. Pearson's correlation coefficient was used for relevant correlations and multiple linear regression to verify the influence of anthropometric variables, body composition and muscle strength of upper and lower limbs. Results: Obese and overweight adolescents had absolute muscle strength values similar to those of the eutrophic adolescents, which were lower when corrected by BM (p<0.001). However, muscle strength related to FFM and localized FFM were similar between groups in both upper and lower limbs. Linear regression showed that BMI explained 59% of the variation in absolute muscle strength of the lower limbs (β=0.59, p<0.05), FFM explained 84% of the variation in absolute muscle strength of the upper limbs (β=0.84, p<0.01) and 68% of the lower limbs (β=0.68; p<0.01), while localized FM was inversely associated in the lower limbs (β=−0.53, p<0.05). Conclusion: Muscle strength of lower and upper limbs, when corrected by localized FFM, does not distinguish between overweight and normal weight adolescents, indicating that obesity does not have a negative effect on generation of muscle strength in obese boys. Level of Evidence III; Case-control study.