Identifying poor cardiorespiratory fitness in overweight and obese children and adolescents by using heart rate variability analysis under resting conditions

Heart Rate Variability and Cardiorespiratory Fitness in Youth Populations: The Impact of Heart Rate on Data Analysis

The positive association between heart rate variability and cardiorespiratory fitness in youth populations is unclear. In this regard, several methodological aspects related to heart rate variability analysis could partially explain the disagreement between studies. To the best of the authors’ knowledge, the influence of the heart rate on data analysis is unclear. In the present short communication, we discuss the impact of the heart rate on the associations between heart rate variability and cardiorespiratory fitness in youth. In addition, we proposed some aspects that should be considered for statistical analyses when the relationship between heart rate variability and cardiorespiratory fitness is studied. Finally, we should acknowledge that these recommendations may be applicable for other health-related outcomes different than cardiorespiratory fitness (e.g., inflammatory markers, cognition, cardiovascular disease status).

Prediction equations for maximal heart rate in obese and nonobese children and adolescents: a systematic review and meta-analysis

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.

Chinese men living in urban areas of Shijiazhuang, Hebei at higher risk of overweight or obesity

Objective

The prevalence of overweight/obesity in China has increased dramatically in recent years; being overweight/obese can increase the risk of type 2 diabetes and cardiovascular disease. The purpose of this study was to determine the population in China at high risk of being overweight or obese, to explore the relationships between various relevant factors and overweight/obesity, and to identify preventive efforts for high-risk populations.

Methods

We administered a questionnaire survey among a group of 536 social workers in Shijiazhuang City in 2017. We used the Pearson chi-square test, Spearman’s rho test, multivariate linear regression, univariate and multivariate logistic regression, and receiver operating characteristic curve analysis to investigate factors that influence overweight/obesity.

Results

The prevalence of overweight/obesity was 13.7% among the study participants. Urban residence, eating speed, number of daily meals, overeating, and a high-fat diet were associated with overweight/obesity. In multivariate linear regression analysis, overweight/obesity was correlated with sex, urban residence, eating speed, number of daily meals, and a high-fat diet.

Conclusion

Among all influencing factors, dietary factors, place of residence, and sex were most closely related to being overweight/obese. Furthermore, living in an urban area and male sex were independent risk factors for being overweight/obese.