Developing new VO2max prediction models from maximal, submaximal and questionnaire variables using support vector machines combined with feature selection

The effects of intermittent hypoxic training on the aerobic capacity of exercisers: a systemic review and meta-analysis

OBJECTIVE

To systematically review the effects of intermittent hypoxic training on the aerobic capacity of exercisers.

METHODS

PubMed, Embase, The Cochrane Library, and Web of Science databases were electronically searched to collect studies on the effects of intermittent hypoxic training on the aerobic capacity of exercisers from January 1, 2000, to January 12, 2023. Two reviewers independently screened the literature, extracted data, and assessed the risk of bias of the included studies. Then, meta-analysis was performed by using Stata SE 16.0 software.

RESULTS

A total of 19 articles from 27 studies were included. The results of the meta-analysis showed that compared with the control group, the intermittent hypoxic training group had significantly increased maximal oxygen uptake [weighted mean difference = 3.20 (95%CI: 1.33 ~ 5.08)] and hemoglobin [weighted mean difference = 0.25 (95%CI: 0.04 ~ 0.45)].

CONCLUSION

Intermittent hypoxic training can significantly improve the aerobic capacity of exercisers. Due to the limited quantity and quality of the included studies, more high-quality studies are needed to verify the above conclusion.

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.

Predicting VO2max in Children and Adolescents Aged between 6 and 17 Using Physiological Characteristics and Participation in Sport Activities: A Cross-Sectional Study Comparing Different Regression Models Stratified by Gender

Background: The aim of this study is to use different regression models to capture the association between cardiorespiratory fitness VO2max (measured in mL/kg/min) and somatometric characteristics and sports activities and making better predictions. Methods: multiple linear regression (MLR), quantile regression (QR), ridge regression (RR), support vector regression (SVR) with three different kernels, artificial neural networks (ANNs), and boosted regression trees (RTs) were compared to explain and predict VO2max and to choose the best performance model. The sample consisted of 4908 children (2314 males and 2594 females) aged between 6 and 17. Cardiorespiratory fitness was assessed by the 20 m maximal multistage shuttle run test and maximal oxygen uptake (VO2max) was calculated. Welch t-tests, Mann−Whitney-U tests, X2 tests, and ANOVA tests were performed. The performance measures were root mean square error (RMSE), mean absolute error (MAE), and coefficient of determination (R2). All analyses were stratified by gender. Results: A comparison of the statistical indices for both the predicted and actual data indicated that in boys, the MLR model outperformed all other models in all indices, followed by the linear SVR model. In girls, the MLR model performed better than the other models in R2 but was outperformed by SVR-RBF in terms of RMSE and MAE. The overweight and obesity categories in both sexes (p < 0.001) and maternal prepregnancy obesity in girls had a significant negative effect on VO2max. Age, weekly football training, track and field, basketball, and swimming had different positive effects based on gender. Conclusion: The MLR model showed remarkable performance against all other models and was competitive with the SVR models. In addition, this study’s data showed that changes in cardiorespiratory fitness were dependent, to a different extent based on gender, on BMI category, weight, height, age, and participation in some organized sports activities. Predictors that are not considered modifiable, such as gender, can be used to guide targeted interventions and policies.

Population and Age-Based Cardiorespiratory Fitness Level Investigation and Automatic Prediction

Maximal oxygen consumption (VO₂max) reflects aerobic capacity and is crucial for assessing cardiorespiratory fitness and physical activity level. The purpose of this study was to classify and predict the population-based cardiorespiratory fitness based on anthropometric parameters, workload, and steady-state heart rate (HR) of the submaximal exercise test. Five hundred and seventeen participants were recruited into this study. This study initially classified aerobic capacity followed by VO₂max predicted using an ordinary least squares regression model with measured VO₂max from a submaximal cycle test as ground truth. Furthermore, we predicted VO₂max in the age ranges 21–40 and above 40. For the support vector classification model, the test accuracy was 75%. The ordinary least squares regression model showed the coefficient of determination (R²) between measured and predicted VO₂max was 0.83, mean absolute error (MAE) and root mean square error (RMSE) were 3.12 and 4.24 ml/kg/min, respectively. R² in the age 21–40 and above 40 groups were 0.85 and 0.75, respectively. In conclusion, this study provides a practical protocol for estimating cardiorespiratory fitness of an individual in large populations. An applicable submaximal test for population-based cohorts could evaluate physical activity levels and provide exercise recommendations.

Recommendations for marathon runners: on the application of recommender systems and machine learning to support recreational marathon runners

Every year millions of people, from all walks of life, spend months training to run a traditional marathon. For some it is about becoming fit enough to complete the gruelling 26.2 mile (42.2 km) distance. For others, it is about improving their fitness, to achieve a new personal-best finish-time. In this paper, we argue that the complexities of training for a marathon, combined with the availability of real-time activity data, provide a unique and worthwhile opportunity for machine learning and for recommender systems techniques to support runners as they train, race, and recover. We present a number of case studies-a mix of original research plus some recent results-to highlight what can be achieved using the type of activity data that is routinely collected by the current generation of mobile fitness apps, smart watches, and wearable sensors.

Estimated Artificial Neural Network Modeling of Maximal Oxygen Uptake Based on Multistage 10-m Shuttle Run Test in Healthy Adults

We aimed to develop an artificial neural network (ANN) model to estimate the maximal oxygen uptake (VO₂max) based on a multistage 10 m shuttle run test (SRT) in healthy adults. For ANN-based VO₂max estimation, 118 healthy Korean adults (59 men and 59 women) in their twenties and fifties (38.3 ± 11.8 years, men aged 37.8 ± 12.1 years, and women aged 38.8 ± 11.6 years) participated in this study; data included age, sex, blood pressure (systolic blood pressure (SBP), diastolic blood pressure (DBP)), waist circumference, hip circumference, waist-to-hip ratio (WHR), body composition (weight, height, body mass index (BMI), percent skeletal muscle, and percent body), 10 m SRT parameters (number of round trips and final speed), and VO₂max by graded exercise test (GXT) using a treadmill. The best estimation results (R² = 0.8206, adjusted R² = 0.7010, root mean square error; RMSE = 3.1301) were obtained in case 3 (using age, sex, height, weight, BMI, waist circumference, hip circumference, WHR, SBP, DBP, number of round trips in 10 m SRT, and final speed in 10 m SRT), while the worst results (R² = 0.7765, adjusted R² = 0.7206, RMSE = 3.494) were obtained for case 1 (using age, sex, height, weight, BMI, number of round trips in 10 m SRT, and final speed in 10 m SRT). The estimation results of case 2 (using age, sex, height, weight, BMI, waist circumference, hip circumference, WHR, number of round trips in 10 m SRT, and final speed in 10 m SRT) were lower (R² = 0.7909, adjusted R² = 0.7072, RMSE = 3.3798) than those of case 3 and higher than those of case 1. However, all cases showed high performance (R²) in the estimation results. This brief report developed an ANN-based estimation model to predict the VO₂max of healthy adults, and the model’s performance was confirmed to be excellent.

Predicting maximal oxygen uptake from a 3-minute progressive knee-ups and step test

Background

Cardiorespiratory fitness assessment is crucial for diagnosing health risks and assessing interventions. Direct measurement of maximum oxygen uptake (V̇O₂ max) yields more objective and accurate results, but it is practical only in a laboratory setting. We therefore investigated whether a 3-min progressive knee-up and step (3MPKS) test can be used to estimate peak oxygen uptake in these settings.

Method

The data of 166 healthy adult participants were analyzed. We conducted a V̇O₂ max test and a subsequent 3MPKS exercise test, in a balanced order, a week later. In a multivariate regression model, sex; age; relative V̇O₂ max; body mass index (BMI); body fat percentage (BF); resting heart rate (HR0); and heart rates at the beginning as well as at the first, second, third, and fourth minutes (denoted by HR0, HR1, HR2, HR3, and HR4, respectively) during a step test were used as predictors. Moreover, R² and standard error of estimate (SEE) were used to evaluate the accuracy of various body composition models in predicting V̇O₂max.

Results

The predicted and actual V̇O₂ max values were significantly correlated (BF% model: R² = 0.624, SEE = 4.982; BMI model: R² = 0.567, SEE = 5.153). The BF% model yielded more accurate predictions, and the model predictors were sex, age, BF%, HR0, ΔHR3−HR0, and ΔHR3−HR4.

Conclusion

In our study, involving Taiwanese adults, we constructed and verified a model to predict V̇O₂ max, which indicates cardiorespiratory fitness. This model had the predictors sex, age, body composition, and heart rate changes during a step test. Our 3MPKS test has the potential to be widely used in epidemiological research to measure V̇O₂ max and other health-related parameters.

Estimating Maximal Oxygen Uptake from the Ratio of Heart Rate at Maximal Exercise to Heart Rate at Rest in Middle-Aged Men

Purpose

To estimate the maximum mass-specific oxygen uptake (VO_2max) from the ratio of the heart rate at maximal exercise (HR_max) to heart rate at rest (HR_rest) in middle-aged men. VO_2max is an essential measure of cardiorespiratory fitness, but it is difficult to utilize in clinical practice. The proportionality factor HR_max to HR_rest is known to approximate 15 in young well-trained adults. Presumably, the same value is inaccurate for middle-aged men.

Materials and Methods

Six-hundred thirty-four men belonging to the Kuopio Ischaemic Heart Disease Risk Factor Study. Their mean age, body mass index (BMI), the daily total physical activity (TPA), VO_2max, HR_max, and HR_rest were: 49.4±6.4 years, 26.3±3.2 kg/m², 48.5±10.1 metabolic equivalent hours per day, 33.7±7.6 mL/min/kg, 170.1±15.4 beats/min, and 63.3±10.8 beats/min. They included never-smokers 38%, former smokers 29%, and current smokers 33%.

Results

The proportionality factor HR_max to HR_rest in around 50-year-old men approximated 12. One year in age, one step change in BMI (normal weight, overweight, obese), smoking status (never, former, current), and TPA (moderately active, active, highly active) reduced the proportionality factor by 0.1, 0.6, 0.4, and 0.1, respectively. The proportionality factor in obese or current smoking middle-aged men was one point lower compared to normal weight or never-smoking peers. This corresponds to approximately 10 years in chronological age.

Conclusions

In around 50-year-old men with no cardiovascular diseases, bronchial asthma, or cancer, the HR_max to HR_rest ratio should be multiplied by approximately 12 to estimate VO_2max. BMI and smoking status can be considered in calculations to improve accuracy.

VO₂FITTING: A Free and Open-Source Software for Modelling Oxygen Uptake Kinetics in Swimming and other Exercise Modalities

The assessment of oxygen uptake (VO₂) kinetics is a valuable non-invasive way to evaluate cardiorespiratory and metabolic response to exercise. The aim of the study was to develop, describe and evaluate an online VO₂ fitting tool (VO₂FITTING) for dynamically editing, processing, filtering and modelling VO₂ responses to exercise. VO₂FITTING was developed in Shiny, a web application framework for R language. Validation VO₂ datasets with both noisy and non-noisy data were developed and applied to widely-used models (n = 7) for describing different intensity transitions to verify concurrent validity. Subsequently, we then conducted an experiment with age-group swimmers as an example, illustrating how VO₂FITTING can be used to model VO₂ kinetics. Perfect fits were observed, and parameter estimates perfectly matched the known inputted values for all available models (standard error = 0; p < 0.001). The VO₂FITTING is a valid, free and open-source software for characterizing VO₂ kinetics in exercise, which was developed to help the research and performance analysis communities.

Predictive Modeling of VO2max Based on 20 m Shuttle Run Test for Young Healthy People

This study presents mathematical models for predicting VO2max based on a 20 m shuttle run and anthropometric parameters. The research was conducted with data provided by 308 young healthy people (aged 20.6 ± 1.6). The research group includes 154 females (aged 20.3 ± 1.2) and 154 males (aged 20.8 ± 1.8). Twenty-four variables were used to build the models, including one dependent variable and 23 independent variables. The predictive methods of analysis include: the classical model of ordinary least squares (OLS) regression, regularized methods such as ridge regression and Lasso regression, artificial neural networks such as the multilayer perceptron (MLP) and radial basis function (RBF) network. All models were calculated in R software (version 3.5.0, R Foundation for Statistical Computing, Vienna, Austria). The study also involved variable selection methods (Lasso and stepwise regressions) to identify optimum predictors for the analysed study group. In order to compare and choose the best model, leave-one-out cross-validation (LOOCV) was used. The paper presents three types of models: for females, males and the whole group. An analysis has revealed that the models for females ( RMSE C V = 4.07 mL·kg−1·min−1) are characterised by a smaller degree of error as compared to male models ( RMSE C V = 5.30 mL·kg−1·min−1). The model accounting for sex generated an error level of RMSE C V = 4.78 mL·kg−1·min−1.

Data fusion of body-worn accelerometers and heart rate to predict VO2max during submaximal running

Maximal oxygen uptake (VO2max) is often used to assess an individual's cardiorespiratory fitness. However, measuring this variable requires an athlete to perform a maximal exercise test which may be impractical, since this test requires trained staff and specialized equipment, and may be hard to incorporate regularly into training programs. The aim of this study is to develop a new model for predicting VO2max by exploiting its relationship to heart rate and accelerometer features extracted during submaximal running. To do so, we analyzed data collected from 31 recreational runners (15 men and 16 women) aged 19-26 years who performed a maximal incremental test on a treadmill. During this test, the subjects' heart rate and acceleration at three locations (the upper back, the lower back and the tibia) were continuously measured. We extracted a wide variety of features from the measurements of the warm-up and the first three stages of the test and employed a data-driven approach to select the most relevant ones. Furthermore, we evaluated the utility of combining different types of features. Empirically, we found that combining heart rate and accelerometer features resulted in the best model with a mean absolute error of 2.33 ml ⋅ kg-1 ⋅ min-1 and a mean absolute percentage error of 4.92%. The model includes four features: gender, body mass, the inverse of the average heart rate and the inverse of the variance of the total tibia acceleration during the warm-up stage of the treadmill test. Our model provides a practical tool for recreational runners in the same age range to estimate their VO2max from submaximal running on a treadmill. It requires two body-worn sensors: a heart rate monitor and an accelerometer positioned on the tibia.