Heart rate recovery to assess fitness: comparison of different calculation methods in a large cross-sectional study

Identifying time-varying dynamics of heart rate and oxygen uptake from single ramp incremental running tests

Objective.The fact that ramp incremental exercise yields quasi-linear responses for pulmonary oxygen uptake (V˙O2) and heart rate (HR) seems contradictory to the well-known non-linear behavior of underlying physiological processes. Prior research highlights this issue and demonstrates how a balancing of system gain and response time parameters causes linearV˙O2responses during ramp tests. This study builds upon this knowledge and extracts the time-varying dynamics directly from HR andV˙O2data of single ramp incremental running tests.Approach.A large-scale open access dataset of 735 ramp incremental running tests is analyzed. The dynamics are obtained by means of 1st order autoregressive and exogenous models with time-variant parameters. This allows for the estimates of time constant (τ) and steady state gain (SSG) to vary with work rate.Main results.As the work rate increases,τ-values increase on average from 38 to 132 s for HR, and from 27 to 35 s forV˙O2. Both increases are statistically significant (p< 0.01). Further, SSG-values decrease on average from 14 to 9 bpm (km·h-1)-1for HR, and from 218 to 144 ml·min-1forV˙O2(p< 0.01 for decrease parameters of HR andV˙O2). The results of this modeling approach are line with literature reporting on cardiorespiratory dynamics obtained using standard procedures.Significance.We show that time-variant modeling is able to determine the time-varying dynamics HR andV˙O2responses to ramp incremental running directly from individual tests. The proposed method allows for gaining insights into the cardiorespiratory response characteristics when no repeated measurements are available.

Comparative assessment of synthetic time series generation approaches in healthcare: leveraging patient metadata for accurate data synthesis

BACKGROUND

Synthetic data is an emerging approach for addressing legal and regulatory concerns in biomedical research that deals with personal and clinical data, whether as a single tool or through its combination with other privacy enhancing technologies. Generating uncompromised synthetic data could significantly benefit external researchers performing secondary analyses by providing unlimited access to information while fulfilling pertinent regulations. However, the original data to be synthesized (e.g., data acquired in Living Labs) may consist of subjects' metadata (static) and a longitudinal component (set of time-dependent measurements), making it challenging to produce coherent synthetic counterparts.

METHODS

Three synthetic time series generation approaches were defined and compared in this work: only generating the metadata and coupling it with the real time series from the original data (A1), generating both metadata and time series separately to join them afterwards (A2), and jointly generating both metadata and time series (A3). The comparative assessment of the three approaches was carried out using two different synthetic data generation models: the Wasserstein GAN with Gradient Penalty (WGAN-GP) and the DöppelGANger (DGAN). The experiments were performed with three different healthcare-related longitudinal datasets: Treadmill Maximal Effort Test (TMET) measurements from the University of Malaga (1), a hypotension subset derived from the MIMIC-III v1.4 database (2), and a lifelogging dataset named PMData (3).

RESULTS

Three pivotal dimensions were assessed on the generated synthetic data: resemblance to the original data (1), utility (2), and privacy level (3). The optimal approach fluctuates based on the assessed dimension and metric.

CONCLUSION

The initial characteristics of the datasets to be synthesized play a crucial role in determining the best approach. Coupling synthetic metadata with real time series (A1), as well as jointly generating synthetic time series and metadata (A3), are both competitive methods, while separately generating time series and metadata (A2) appears to perform more poorly overall.

Prediction of peak oxygen consumption using cardiorespiratory parameters from warmup and submaximal stage of treadmill cardiopulmonary exercise test

This study investigates the quality of peak oxygen consumption (VO2peak) prediction based on cardiac and respiratory parameters calculated from warmup and submaximal stages of treadmill cardiopulmonary exercise test (CPET) using machine learning (ML) techniques and assesses the importance of respiratory parameters for the prediction outcome. The database consists of the following parameters: heart rate (HR), respiratory rate (RespRate), pulmonary ventilation (VE), oxygen consumption (VO2) and carbon dioxide production (VCO2) obtained from 369 treadmill CPETs. Combinations of features calculated based on the HR, VE and RespRate time-series from different stages of CPET were used to create 11 datasets for VO2peak prediction. Thirteen ML algorithms were employed, and model performances were evaluated using cross-validation with mean absolute percentage error (MAPE), R2 score, mean absolute error (MAE), and root mean squared error (RMSE) calculated after each iteration of the validation. The results demonstrated that incorporating respiratory-based features improves the prediction of VO2peak. The best results in terms of R2 score (0.47) and RMSE (5.78) were obtained for the dataset which included both cardiac- and respiratory-based features from CPET up to 85% of age-predicted HRmax, while the best results in terms of MAPE (10.5%) and MAE (4.63) were obtained for the dataset containing cardiorespiratory features from the last 30 seconds of warmup. The study showed the potential of using ML models based on cardiorespiratory features from submaximal tests for prediction of VO2peak and highlights the importance of the monitoring of respiratory signals, enabling to include respiratory parameters into the analysis. Presented approach offers a feasible alternative to direct VO2peak measurement, especially when specialized equipment is limited or unavailable.

The potential benefits of assessing post-cardiopulmonary exercise testing (CPET) in aging: a narrative review

Cardiopulmonary exercise testing (CPET) is an important tool to measure the cardiopulmonary fitness of an individual and has been widely used in athletic, clinical and research settings. Most CPET focus on analyzing physiological responses during exercise. We contend that the post-CPET recovery physiological responses offer further diagnostic and prognostic information about the health of the cardiopulmonary and metabolic systems, especially when testing apparently healthy middle-aged and older adults. However, there are limited studies that investigate physiological responses during the post-CPET recovery, and even less so in middle-aged and older adults. Therefore, this current review is aimed at discussing the contribution of post-CPET recovery parameters to cardiopulmonary health and their potential applications in aging populations. In addition to the existing methods, we propose to examine the aerobic and anaerobic recovery threshold post-CPET as novel potential diagnostic and/or prognostic tools.

Heart Rate Variability Based Estimation of Maximal Oxygen Uptake in Athletes Using Supervised Regression Models

Wearable Heart Rate monitors are used in sports to provide physiological insights into athletes' well-being and performance. Their unobtrusive nature and ability to provide reliable heart rate measurements facilitate the estimation of cardiorespiratory fitness of athletes, as quantified by maximum consumption of oxygen uptake. Previous studies have employed data-driven models which use heart rate information to estimate the cardiorespiratory fitness of athletes. This signifies the physiological relevance of heart rate and heart rate variability for the estimation of maximal oxygen uptake. In this work, the heart rate variability features that were extracted from both exercise and recovery segments were fed to three different Machine Learning models to estimate maximal oxygen uptake of 856 athletes performing Graded Exercise Testing. A total of 101 features from exercise and 30 features from recovery segments were given as input to three feature selection methods to avoid overfitting of the models and to obtain relevant features. This resulted in the increase of model's accuracy by 5.7% for exercise and 4.3% for recovery. Further, post-modelling analysis was performed to remove the deviant points in two cases, initially in both training and testing and then only in training set, using k-Nearest Neighbour. In the former case, the removal of deviant points led to a reduction of 19.3% and 18.0% in overall estimation error for exercise and recovery, respectively. In the latter case, which mimicked the real-world scenario, the average R value of the models was observed to be 0.72 and 0.70 for exercise and recovery, respectively. From the above experimental approach, the utility of heart rate variability to estimate maximal oxygen uptake of large population of athletes was validated. Additionally, the proposed work contributes to the utility of cardiorespiratory fitness assessment of athletes through wearable heart rate monitors.

Prediction of Relevant Training Control Parameters at Individual Anaerobic Threshold without Blood Lactate Measurement

BACKGROUND

Active exercise therapy plays an essential role in tackling the global burden of obesity. Optimizing recommendations in individual training therapy requires that the essential parameters heart rate HR(IAT) and work load (W/kg(IAT) at individual anaerobic threshold (IAT) are known. Performance diagnostics with blood lactate is one of the most established methods for these kinds of diagnostics, yet it is also time consuming and expensive.

METHODS

To establish a regression model which allows HR(IAT) and (W/kg(IAT) to be predicted without measuring blood lactate, a total of 1234 performance protocols with blood lactate in cycle ergometry were analyzed. Multiple linear regression analyses were performed to predict the essential parameters (HR(IAT)) (W/kg(IAT)) by using routine parameters for ergometry without blood lactate.

RESULTS

HR(IAT) can be predicted with an RMSE of 8.77 bpm (p < 0.001), R² = 0.799 (|R| = 0.798) without performing blood lactate diagnostics during cycle ergometry. In addition, it is possible to predict W/kg(IAT) with an RMSE (root mean square error) of 0.241 W/kg (p < 0.001), R² = 0.897 (|R| = 0.897).

CONCLUSIONS

It is possible to predict essential parameters for training management without measuring blood lactate. This model can easily be used in preventive medicine and results in an inexpensive yet better training management of the general population, which is essential for public health.

Heart Rate Recovery (HRR) Is Not a Singular Predictor for Physical Fitness

BACKGROUND

For optimal recommendations in cardiovascular training for the general population, knowing the essential parameters for physical fitness is required. Heart rate recovery (HRR) is an easy-to-measure parameter and is discussed to derive the physical fitness of an individual subject. This study evaluates HRR as a potential physical fitness parameter for public health programs, as it is measured in every ergometry.

METHODS

In this retrospective cross-sectional study, we analyzed HRR regarding physical fitness (W/kg (IAT: individual anaerobic threshold)). In total, we analyzed 1234 performance protocols in cycle ergometry. Significance tests (p < 0.001) and multiple linear regression were performed.

RESULTS

The analysis of HRR and weight-related performance showed a significant correlation with a moderate coefficient of determination (R² = 0.250). The coefficient of determination increases from very weak correlation levels at 1 min post-workout towards weak to moderate levels of correlation at 5 min post-workout.

CONCLUSIONS

In this study HRR and the weight-related performance at the IAT showed a significant correlation with a mean strength. Thus, a prediction or conclusion on physical performance based singularly on HRR decrease is not recommended. However, in preventive medicine, HRR should be measured and observed on a long-term basis, for analysis of vagal activity and to draw to inferences of mortality.

Stratified Multivariate Multiscale Dispersion Entropy for Physiological Signal Analysis

Multivariate entropy quantification algorithms are becoming a prominent tool for the extraction of information from multi-channel physiological time-series. However, in the analysis of physiological signals from heterogeneous organ systems, certain channels may overshadow the patterns of others, resulting in information loss. Here, we introduce the framework of Stratified Entropy to prioritize each channels' dynamics based on their allocation to respective strata, leading to a richer description of the multi-channel time-series. As an implementation of the framework, three algorithmic variations of the Stratified Multivariate Multiscale Dispersion Entropy are introduced. These variations and the original algorithm are applied to synthetic time-series, waveform physiological time-series, and derivative physiological data. Based on the synthetic time-series experiments, the variations successfully prioritize channels following their strata allocation while maintaining the low computation time of the original algorithm. In experiments on waveform physiological time-series and derivative physiological data, increased discrimination capacity was noted for multiple strata allocations in the variations when benchmarked to the original algorithm. This suggests improved physiological state monitoring by the variations. Furthermore, our variations can be modified to utilize a priori knowledge for the stratification of channels. Thus, our research provides a novel approach for the extraction of previously inaccessible information from multi-channel time series acquired from heterogeneous systems.

Decrease of heart rate variability during exercise: An index of cardiorespiratory fitness

The present study proposes to measure and quantify the heart rate variability (HRV) changes during effort as a function of the heart rate and to test the capacity of the produced indices to predict cardiorespiratory fitness measures. Therefore, the beat-to-beat cardiac time interval series of 18 adolescent athletes (15.2 ± 2.0 years) measured during maximal graded effort test were detrended using a dynamical first-order differential equation model. HRV was then calculated as the standard deviation of the detrended RR intervals (SDRR) within successive windows of one minute. The variation of this measure of HRV during exercise is properly fitted by an exponential decrease of the heart rate: the SDRR is divided by 2 every increase of heart rate of 20 beats/min. The HR increase necessary to divide by 2 the HRV is linearly inversely correlated with the maximum oxygen consumption (r = -0.60, p = 0.006), the maximal aerobic power (r = -0.62, p = 0.006), and, to a lesser extent, to the power at the ventilatory thresholds (r = -0.53, p = 0.02 and r = -0.47, p = 0.05 for the first and second threshold). It indicates that the decrease of the HRV when the heart rate increases is faster among athletes with better fitness. This analysis, based only on cardiac measurements, provides a promising tool for the study of cardiac measurements generated by portable devices.

Heart rate recovery as an assessment of cardiorespiratory fitness in young adults

Background

Cardiorespiratory fitness, typically measured as peak oxygen uptake (VO2peak) during maximal graded exercise testing (GXTmax), is a predictor of morbidity, mortality, and cardiovascular disease. However, measuring VO2peak is costly and inconvenient and thus not widely used in clinical settings. Alternatively, postexercise heart rate recovery (HRRec), which is an index of vagal reactivation, is a valuable assessment of VO2peak in older adults and athletes. However, the validity of HRRec as a clinical indicator of cardiorespiratory fitness in young, sedentary adults, who are a rapidly growing population at risk for developing obesity and cardiovascular disease, has not been fully elucidated.

Methods

We investigated the association between cardiorespiratory fitness, measured by VO2peak (mL·kg-1·min-1), and HRRec measures after a GXTmax in 61 young (25.2 ± 6.1 years), sedentary adults (40 females) using 3 methods. We examined the relationship between VO2peak and absolute (b·min-1) and relative (%) HRRec measures at 1, 2, and 3 min post GXTmax, as well as a measure of the slow component HRRec (HRRec 1 min minus HRR 2 min), using Pearson's correlation analysis.

Results

VO2peak (36.5 ± 7.9 mL·kg-1·min-1) was not significantly correlated with absolute HRRec at 1 min (r = 0.18), 2 min (r = 0.04) or 3 min (r = 0.01). We also found no significant correlations between VO2peak and relative HRRec at 1 min (r = 0.09), 2 min (r = -0.06) or 3 min (r = -0.10). Lastly, we found no correlation between the measure of the slow component HRRec and VO2peak (r = -0.14).

Conclusions

Our results indicate that HRRec measures are not a valid indicator of cardiorespiratory fitness in young, sedentary adults.

TEST OF PHYSIOLOGICAL PERFORMANCE: RATIONALE AND FEASIBILITY

Rigorous clinical evaluation of the physiological performance is currently performed with complex and long procedures which need expensive technology and skilled operators. In a wide range of situations (frail patients, daily clinical practice, etc.), these approaches are difficult to be applied and simpler tests, with a lack of scientific background, are mandatory. To avoid these problems, we propose a test (test of physiological performance (TOPP)) to evaluate the physiological behavior of a subject, in a really easy and safe clinical setting, measuring only the heart rate. The subject is submitted to an active standing-up test and then two submaximal exercises (with a low power load) on a cycle-ergometer. The heart rate modifications due to each submaximal step are analyzed by exponential interpolation to calculate the ascending and descending time constants and evaluate the way each subject adapts his heart rate to work. The standard deviation of the RR for each stationary phase (warm-up, load, recovery) was calculated as an index of short-term variability. Then a standard Fourier analysis of the stationary periods of the standing-up procedures allows to quickly and easily evaluate the autonomic nervous activation. We tested the protocol on five healthy subjects to verify the feasibility and the acceptance of the procedure. The five subjects demonstrated a good tolerance of the entire procedure. The standing-up showed a behavior of the autonomic system consistent with the physiology (with an increase in sympathetic activation in the passage to standing position). The analysis of the two submaximal steps highlights how younger and trained subjects present lower heart rates (both in the ascending phase and in the recovery) with a quicker adaptation ability (smaller time constants) consistent with what is expected. The short-term variability of heart rate is greater in young and trained subjects, thus confirming how the sympatho-vagal balance, in these subjects, is more dynamic. The proposed test is well tolerated by the subjects and the results, albeit in a small cohort of healthy volunteers, are consistent with what is expected from physiology and is already present in the literature. Our work aims to be a proposal with a feasibility check of a method for evaluating performance. The work to be done for the clinical validation of the TOPP is still long, but we are aware that it can give important results and that the TOPP can become an effective tool for the assessment of the physiological performance even of fragile subjects.