RESUMEN
Introduction: Obesity is considered to be a risk factor for a variety of cardiovascular conditions. Various markers for obesity are used to evaluate effect of obesity on cardiovascular autonomic activity. In light of conflicting reports on effect of obesity on heart rate variability (HRV), use of obesity indices, and the effect of physical activity on HRV, we evaluated autonomic activity in young Indian obese adults using revised Indian and World Health Organization (WHO) body mass index (BMI) guidelines for obesity, waist circumference (WC), and waist-hip ratio (WHR) taking into consideration the level of physical activity. Methods: The study was conducted on 91 young healthy adults. Height, weight, waist, and hip circumference were recorded to determine BMI and WHR. Five-minute electrocardiogram (ECG) was recorded for assessment of HRV. Physical activity was assessed by the WHO Global Physical Activity Questionnaire (GPAQ). Results: Waist circumference showed a negative correlation with the time domain parameters of HRV and high frequency normalized units (HFnu) while a positive correlation with low frequency normalized units (LFnu). In multiple linear regression analysis, time domain indices, HFnu and total power decreased while LFnu increased with an increase in WC. The result was supported by the similar effect of waist-hip ratio categories on HRV in analysis of covariance (ANCOVA). Physical activity had no effect on HRV. Conclusion: Central obesity parameters are better predictors of effect of obesity on HRV independent of the effect of physical activity.
RESUMEN
Coronavirus disease, a novel severe acute respiratory syndrome (SARS COVID-19), has become a global health concern due to its unpredictable nature and lack of adequate medicines. Machine Learning (ML) models could be effective in identifying the most critical factors which are responsible for the overall fatalities caused by COVID-19. The functional capabilities of ML models in epidemiological research, especially for COVID-19, are not substantially explored. To bridge this gap, this study has adopted two advanced ML models, viz. Random Forest (RF) and Gradient Boosted Machine (GBM), to perform the regression modelling and provide subsequent interpretation. Five successive steps were followed to carry out the analysis: (1) identification of relevant key explanatory variables; (2) application of data dimensionality reduction for eliminating redundant information; (3) utilizing ML models for measuring relative influence (RI) of the explanatory variables; (4) evaluating interconnections between and among the key explanatory variables and COVID-19 case and death counts; (5) time series analysis for examining the rate of incidences of COVID-19 cases and deaths. Among the explanatory variables considered in this study, air pollution, migration, economy, and demographic factor were found to be the most significant controlling factors. Since a very limited research is available to discuss the superiority of ML models for identifying the key determinants of COVID-19, this study could be a reference for future public health research. Additionally, all the models and data used in this study are open source and freely available, thereby, reproducibility and scientific replication will be achievable easily.