Effect of physical exercise on autonomic regulation of heart rate

Abstract Regular aerobic training has been suggested to protect the heart by increasing cardiac vagal activity. The aims of this study were to evaluate the autonomic regulation of heart rate (HR) during and after exercise, during aerobic training interventions and to study the association between autonomic regulation and the training response in healthy male subjects. HR variability assessment was used to study the effects of exercise on autonomic regulation of HR. The whole study population consisted of 70 volunteer male subjects (age 36 ± 10 years). The recovery of the autonomic nervous system after prolonged exhaustive exercise was studied in a group of 10 subjects. The training interventions included 51 subjects. The effects of training volume on autonomic regulation were assessed (n = 46) during a controlled eight-week training intervention. The association between training and autonomic regulation was studied (n = 24) during a ten-month period of home-based training based on the American College of Sports Medicine recommendations. Finally, the association between autonomic regulation and the individual training response was analysed (n = 51) after eight weeks of controlled training. The recovery rate of vagally mediated high-frequency (HF) power of HR variability after prolonged exhaustive exercise was associated with physical fitness (r = -0.71, P < 0.016). Moderate (3 hours/week) and high-volume (6 hours/week) aerobic training results in a similar increase in HR variability indices. HF power increased from 6.19 ± 1.02 to 6.76 ± 0.96 ln ms2 (P < 0.001) and from 6.61 ± 1.01 to 7.12 ± 0.92 ln ms2 (P < 0.001) after moderate and high-volume training, respectively. During the home-based training program, the changes in HF power were associated with the changes in the fitness (r = 0.44, P < 0.05), body mass index (r = -0.44, P < 0.05) and the amount of training (r = 0.41, p < 0.05). Finally, a significant correlation was observed between the training response and the baseline HF power (r = 0.52, P = 0.001). HF power accounted for 27 % of the change as an independent predictor of the aerobic training response. In conclusion, a highly controlled aerobic training intervention of eight weeks, including six 30-min sessions a week at an intensity of 70–80 % of maximum HR, is a sufficient intervention to increase cardiac vagal outflow and the offered home-based training according the current guidelines maintains the high cardiac vagal outflow. Secondly, high vagal activity at baseline is associated with the improvement in aerobic fitness caused by aerobic training, suggesting that the cardiovascular autonomic function is an important determinant of the response to aerobic training

Liikuntalääketiede elintapasairauksien ehkäisyssä ja hoidossa:pätkittäin arjessa vai kaasu pohjassa?

Tiivistelmä Säännöllisen liikunnan sisällyttäminen viikoittaisen arjen rytmiin lisää kiistatta hyvinvointia ja edistää parempaa terveyttä tarkasteltiinpa sitä elintapasairauksien ennaltaehkäisyn, hoidon tai kuntoutuksen näkökulmasta

Introduction to the research topic: the role of physical fitness on cardiovascular responses to stress

[Extract] This e-book is the culmination of countless hours of meticulous work by global scientists. We would like to thank the researchers for their great contributions to this hot topic. The combination of these studies reflects the importance of the topic amongst researchers and practitioners and the wide interest from numerous laboratories around the world. The contributions include a variety of formats including five original investigations, three review articles, one opinion article and a hypothesis and theory article. Notably, these contributions included both human and animal models that encompassed a range of techniques from molecular mechanisms to real life interventions thus reinforcing the translational approach for the understanding of cardiovascular responses to stress

Commentaries on point: Counterpont: Exercise training-induced bradycardia

[Extract] TO THE EDITOR: In the recent Point:Counterpoint debate (2, 3), both research groups argued that training-induced bradycardia was a result of changes in intrinsic, sinoatrial node firing (i.e., intrinsic rate) or cardiac autonomic/parasympathetic regulation. While Billman (2) stated the possibility of a combination of these mechanisms, no further discussion of this was provided by either research group. Rather, the discussion focused on either mechanism and we would urge researchers to consider a more complex scenario – contribution of either mechanism that is moderated by other factors, or both mechanisms, potentially in combination with other elements (e.g., cardiac tructural changes). For example, training-induced bradycardia was reported in young adults with no changes in heart rate variability (HRV)(4), supporting an intrinsic rate mechanism. However, bradycardia was induced similarly with enhanced HRV in young adults following high-intensity exercise, supporting a cardiac autonomic mechanism (5)

The Role of Physical Fitness on Cardiovascular Responses to Stress

Cardiovascular responses to physical and/ or mental stressors has been a topic of great interest for some time. For example, significant changes of cardiovascular control and reactivity have been highlighted as important mechanisms for the protective effect of exercise as a simple and effective, non medical therapy for many pathologies. However, despite the great number of studies performed to date (e.g. >54,000 entries in Pubmed for "cardiovascular stress"), important questions of the role stress has on cardiovascular function still remain. For instance, What factors account for the different cardiovascular responses between mental and physical stressors? How do these different components of the cardiovascular system interact during stress? Which cardiovascular responses to stress are the most important for identifying normal, depressed, and enhanced cardiovascular function? Can these stress-induced responses assist with patient diagnosis and prognosis? What impact does physical fitness have on the relationship between cardiovascular function and health? The current topic examined our current understanding of cardiovascular responses to stress and the significant role that physical fitness has on these responses for improved function and health. Manuscripts focusing on heart rate variability (HRV), heart rate recovery, and other novel cardiovascular assessments were especially encouraged

Characterization and reduction of exercise-based motion influence on heart rate variability using accelerator signals and channel decoding in the time–frequency domain

Abstract Objective: Heart rate variability (HRV) is defined as the variation of the heart’s beat to beat time intervals. Although HRV has been studied for decades, its response to stress tests and off-rest measurements is still under investigation. In this paper, we studied the influence of motion on HRV throughout different exercise tests, including a maximal running of healthy recreational runners, cycling, and walking tests of healthy subjects. Approach: In our proposed method, we utilized the motion trajectory (which is known to exist partially in HRV) measured by a three-channel accelerator (ACC). We then estimated their shares in HRV using a wearable electrocardiogram (ECG) and an error-correcting problem formulation. In this method, we characterized the motion components of three orthogonal directions induced into the HRV signal, and then we suppressed the estimated motion artefact to construct a motion-attenuated spectrogram. Main results and Significance: Our analysis showed that HRV in the exercise context is susceptible to motion artefacts. Furthermore, the interpretation of autonomic nervous system (ANS) activity and HRV indices throughout exercise has a high margin of error depending on the intensity level, type of exercise, and motion trajectory. Our experiment on 84 healthy subjects throughout mid-intensity cycling and walking tests showed 39% and 32% influence on average, respectively. In addition, our proposed method revealed through a maximal running test with 11 runners that motion can describe on average 20%–40% of the HRV high-frequency (HF) energy at different workloads of running

Spectral fusion-based breathing frequency estimation:experiment on activities of daily living

Abstract Background: We study the estimation of breathing frequency (BF) derived from wearable single-channel ECG signal in the context of mobile daily life activities. Although respiration effects on heart rate variability and ECG morphology have been well established, studies on ECG-derived respiration in daily living settings are scarce; possibly due to considerable amount of disturbances in such data. Yet, unobtrusive BF estimation during everyday activities can provide vital information for both disease management and athletic performance optimization. Method and data: For robust ECG-derived BF estimation, we combine the respiratory information derived from R–R interval (RRI) variability and morphological scale variation of QRS complexes (MSV), acquired from ECG signals. Two different fusion techniques are applied on MSV and RRI signals: cross-power spectral density (CPSD) estimation and power spectrum multiplication (PSM). The algorithms were tested on large sets of data collected from 67 participants during office, household and sport activities, simulating daily living activities. We use spirometer reference BF to evaluate and compare our estimations made by different models. Results and conclusion: PSM acquires the least average error of BF estimation, %D²ᵟ=9.86 and %E=9.45, compared to the reference spirometer values. PSM offers approximately 25 and 75% less error in comparison with the CPSD fusion estimation and the estimation by those two exclusive sources, respectively. Our results demonstrate the superiority of both of the fusion approaches, compared to the estimation derived from either of RRI or MSV signals exclusively

Methods of assessment of the post-exercise cardiac autonomic recovery: additional important factors to be considered.

[Extract] The review from Peçanha T. and colleagues [5] has made an important contribution to the applicability of post-exercise, cardiac autonomic assessment utilising heart rate recovery (HRR) and heart rate variability (HRV). We congratulate these authors and would like to highlight important factors that should also be considered in conjunction with this work

Spectral data fusion for robust ECG-derived respiration with experiments in different physical activity levels

Abstract In this paper, we study instant respiratory frequency extraction using single-channel electrocardiography (ECG) during mobile conditions such as high intensity exercise or household activities. Although there are a variety of ECG-derived respiration (EDR) methods available in the literature, their performance during such activities is not very well-studied. We propose a technique to boost the robustness and reliability of widely used and computationally efficient EDR methods, aiming to qualify them for ambulatory and daily monitoring. We fuse two independent sources of respiratory information available in ECG signal, including respiratory sinus arrhythmia (RSA) and morphological change of ECG time series, to enhance the accuracy and reliability of instant breathing rate estimation during ambulatory measurements. Our experimental results show that the fusion method outperforms individual methods in four different protocols, including household and sport activities

Machine learning models in predicting health care costs in patients with a recent acute coronary syndrome:a prospective pilot study

Abstract Background: Health care budgets are limited, requiring the optimal use of resources. Machine learning (ML) methods may have an enormous potential for effective use of health care resources. Objective: We assessed the applicability of selected ML tools to evaluate the contribution of known risk markers for prognosis of coronary artery disease to predict health care costs for all reasons in patients with a recent acute coronary syndrome (n = 65, aged 65 ± 9 years) for 1-year follow-up. Methods: Risk markers were assessed at baseline, and health care costs were collected from electronic health registries. The Cross-decomposition algorithms were used to rank the considered risk markers based on their impacts on variances. Then regression analysis was performed to predict costs by entering the first top-ranking risk marker and adding the next-best markers, one by one, to build up altogether 13 predictive models. Results: The average annual health care costs were €2601 ± €5378 per patient. The Depression Scale showed the highest predictive value (r = 0.395), accounting for 16% of the costs (P = .001). When the next 2 ranked markers (LDL cholesterol, r = 0.230; and left ventricular ejection fraction, r = -0.227, respectively) were added to the model, the predictive value was 24% for the costs (P = .001). Conclusion: Higher depression score is the primary variable forecasting health care costs in 1-year follow-up among acute coronary syndrome patients. The ML tools may help decision-making when planning optimal utilization of treatment strategies