Periodized Aerobic Interval Training Modifies Geometric Indices of Heart Rate Variability in Metabolic Syndrome

Chronic Effects of Different Intensities of Interval Training on Hemodynamic, Autonomic and Cardiorespiratory Variables of Physically Active Elderly People

Interval training (IT) is a very efficient method. We aimed to verify the chronic effects of IT with different intensities on hemodynamic, autonomic and cardiorespiratory variables in the elderly. Twenty-four physically active elderly men participated in the study and were randomized into three groups: Training Group A (TG_A, n = 8), Training Group B (TG_B, n = 8) and control group (CG, n = 8). The TG_A and TG_B groups performed 32 sessions (48 h interval). TG_A presented 4 min (55 to 60% of HRmax) and 1 min (70 to 75% of HRmax). The TGB training groups performed the same protocol, but performed 4 min at 45 to 50% HRmax and 1 min at 60 to 65% HRmax. Both training groups performed each set six times, totaling 30 min per session. Assessments were performed pre (baseline) after the 16th and 32nd intervention session. The CG performed only assessments. Hemodynamic, autonomic and cardiorespiratory (estimated VO_2max) variables were evaluated. There were no significant differences between protocols and times (p > 0.05). However, the effect size and percentage delta indicated positive clinical outcomes, indicating favorable responses of IT. IT may be a strategy to improve hemodynamic, autonomic and cardiorespiratory behavior in healthy elderly people.

A Proposal for a Data-Driven Approach to the Influence of Music on Heart Dynamics

Electrocardiographic signals (ECG) and heart rate viability measurements (HRV) provide information in a range of specialist fields, extending to musical perception. The ECG signal records heart electrical activity, while HRV reflects the state or condition of the autonomic nervous system. HRV has been studied as a marker of diverse psychological and physical diseases including coronary heart disease, myocardial infarction, and stroke. HRV has also been used to observe the effects of medicines, the impact of exercise and the analysis of emotional responses and evaluation of effects of various quantifiable elements of sound and music on the human body. Variations in blood pressure, levels of stress or anxiety, subjective sensations and even changes in emotions constitute multiple aspects that may well-react or respond to musical stimuli. Although both ECG and HRV continue to feature extensively in research in health and perception, methodologies vary substantially. This makes it difficult to compare studies, with researchers making recommendations to improve experiment planning and the analysis and reporting of data. The present work provides a methodological framework to examine the effect of sound on ECG and HRV with the aim of associating musical structures and noise to the signals by means of artificial intelligence (AI); it first presents a way to select experimental study subjects in light of the research aims and then offers possibilities for selecting and producing suitable sound stimuli; once sounds have been selected, a guide is proposed for optimal experimental design. Finally, a framework is introduced for analysis of data and signals, based on both conventional as well as data-driven AI tools. AI is able to study big data at a single stroke, can be applied to different types of data, and is capable of generalisation and so is considered the main tool in the analysis.