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Wenzel C, Liebig T, Swoboda A, Smolareck R, Schlagheck ML, Walzik D, Groll A, Goulding RP, Zimmer P. Machine learning predicts peak oxygen uptake and peak power output for customizing cardiopulmonary exercise testing using non-exercise features. Eur J Appl Physiol 2024:10.1007/s00421-024-05543-x. [PMID: 38958720 DOI: 10.1007/s00421-024-05543-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/28/2024] [Accepted: 06/22/2024] [Indexed: 07/04/2024]
Abstract
PURPOSE Cardiopulmonary exercise testing (CPET) is considered the gold standard for assessing cardiorespiratory fitness. To ensure consistent performance of each test, it is necessary to adapt the power increase of the test protocol to the physical characteristics of each individual. This study aimed to use machine learning models to determine individualized ramp protocols based on non-exercise features. We hypothesized that machine learning models will predict peak oxygen uptake ( V ˙ O2peak) and peak power output (PPO) more accurately than conventional multiple linear regression (MLR). METHODS The cross-sectional study was conducted with 274 (♀168, ♂106) participants who performed CPET on a cycle ergometer. Machine learning models and multiple linear regression were used to predict V ˙ O2peak and PPO using non-exercise features. The accuracy of the models was compared using criteria such as root mean square error (RMSE). Shapley additive explanation (SHAP) was applied to determine the feature importance. RESULTS The most accurate machine learning model was the random forest (RMSE: 6.52 ml/kg/min [95% CI 5.21-8.17]) for V ˙ O2peak prediction and the gradient boosting regression (RMSE: 43watts [95% CI 35-52]) for PPO prediction. Compared to the MLR, the machine learning models reduced the RMSE by up to 28% and 22% for prediction of V ˙ O2peak and PPO, respectively. Furthermore, SHAP ranked body composition data such as skeletal muscle mass and extracellular water as the most impactful features. CONCLUSION Machine learning models predict V ˙ O2peak and PPO more accurately than MLR and can be used to individualize CPET protocols. Features that provide information about the participant's body composition contribute most to the improvement of these predictions. TRIAL REGISTRATION NUMBER DRKS00031401 (6 March 2023, retrospectively registered).
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Affiliation(s)
- Charlotte Wenzel
- Institute for Sport and Sport Science, Performance and Health (Sports Medicine), TU Dortmund University, Dortmund, Germany
| | - Thomas Liebig
- Institute for Computer Science, Department of Artificial Intelligence, TU Dortmund University, Dortmund, Germany
| | - Adrian Swoboda
- Institute for Training Optimization for Sport and Health, iQ Athletik, Frankfurt am Main, Germany
| | - Rika Smolareck
- Institute for Sport and Sport Science, Performance and Health (Sports Medicine), TU Dortmund University, Dortmund, Germany
| | - Marit L Schlagheck
- Institute for Sport and Sport Science, Performance and Health (Sports Medicine), TU Dortmund University, Dortmund, Germany
| | - David Walzik
- Institute for Sport and Sport Science, Performance and Health (Sports Medicine), TU Dortmund University, Dortmund, Germany
| | - Andreas Groll
- Department of Statistics, Statistical Methods for Big Data, TU Dortmund University, Dortmund, Germany
| | - Richie P Goulding
- Faculty of Behavioral and Movement Sciences, Department of Human Movement Sciences, Vrije Universiteit Amsterdam, Amsterdam Movement Sciences, Amsterdam, The Netherlands
| | - Philipp Zimmer
- Institute for Sport and Sport Science, Performance and Health (Sports Medicine), TU Dortmund University, Dortmund, Germany.
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Zadarko-Domaradzka M, Sobolewski M, Zadarko E. Comparison of Several Anthropometric Indices Related to Body Fat in Predicting Cardiorespiratory Fitness in School-Aged Children-A Single-Center Cross-Sectional Study. J Clin Med 2023; 12:6226. [PMID: 37834868 PMCID: PMC10573168 DOI: 10.3390/jcm12196226] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/05/2023] [Revised: 09/21/2023] [Accepted: 09/25/2023] [Indexed: 10/15/2023] Open
Abstract
Body fat (BF) and cardiorespiratory fitness (CRF) are important health markers that ought to be considered in screening exams. The aim of this study was to assess the value of six indicators, i.e., tri-ponderal mass index (TMI), relative fat mass (RFM), waist-BMI ratio, waist-to-height ratio (WHtR), waist-to-hip ratio (WHR) and body mass index (BMI) in predicting CRF in school-aged children. The analysis was based on the data coming from the examination of 190 children participating in school physical education (PE) classes. Their body weight (BW) and height (BH), waist and hip circumference (WC; HC) and percentage of body fat (%BF) were measured; the CRF test was performed with the use of the 20 m shuttle run test (20 mSRT); peak heart rate (HRpeak) was measured; TMI, relative fat mass pediatric (RFMp), waist-BMI ratio, WHtR, BMI and WHR were calculated. Statistical analysis was mainly conducted using regression models. The developed regression models, with respect to the sex and age of the children, revealed RFMp as the strongest CRF indicator (R2 = 51.1%) and WHR as well as waist-BMI ratio as the weakest ones (R2 = 39.2% and R2 = 40.5%, respectively). In predicting CRF in school-aged children, RFMp turned out to be comparable to body fat percentage obtained by means of the bioimpedance analysis (BIA) (R2 = 50.3%), and as such it can be used as a simple screening measure in prophylactic exams of school children. All of these models were statistically significant (p < 0.001).
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Affiliation(s)
- Maria Zadarko-Domaradzka
- Institute of Physical Culture Sciences, College of Medical Sciences, Rzeszow University, 35-959 Rzeszow, Poland;
| | - Marek Sobolewski
- Department of Quantitative Methods Rzeszow, University of Technology, 35-959 Rzeszow, Poland;
| | - Emilian Zadarko
- Institute of Physical Culture Sciences, College of Medical Sciences, Rzeszow University, 35-959 Rzeszow, Poland;
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Balakarthikeyan V, Jais R, Vijayarangan S, Sreelatha Premkumar P, Sivaprakasam M. Heart Rate Variability Based Estimation of Maximal Oxygen Uptake in Athletes Using Supervised Regression Models. SENSORS (BASEL, SWITZERLAND) 2023; 23:3251. [PMID: 36991963 PMCID: PMC10054075 DOI: 10.3390/s23063251] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 01/31/2023] [Revised: 03/04/2023] [Accepted: 03/09/2023] [Indexed: 06/19/2023]
Abstract
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.
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Affiliation(s)
- Vaishali Balakarthikeyan
- Department of Electrical Engineering, Indian Institute of Technology Madras, Chennai 600036, India; (R.J.); (S.V.); (M.S.)
- Healthcare Technology Innovation Centre (HTIC), Chennai 600113, India;
| | - Rohan Jais
- Department of Electrical Engineering, Indian Institute of Technology Madras, Chennai 600036, India; (R.J.); (S.V.); (M.S.)
| | - Sricharan Vijayarangan
- Department of Electrical Engineering, Indian Institute of Technology Madras, Chennai 600036, India; (R.J.); (S.V.); (M.S.)
- Healthcare Technology Innovation Centre (HTIC), Chennai 600113, India;
| | | | - Mohanasankar Sivaprakasam
- Department of Electrical Engineering, Indian Institute of Technology Madras, Chennai 600036, India; (R.J.); (S.V.); (M.S.)
- Healthcare Technology Innovation Centre (HTIC), Chennai 600113, India;
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Carayanni V, Bogdanis GC, Vlachopapadopoulou E, Koutsouki D, Manios Y, Karachaliou F, Psaltopoulou T, Michalacos S. Predicting VO 2max 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. CHILDREN (BASEL, SWITZERLAND) 2022; 9:children9121935. [PMID: 36553378 PMCID: PMC9776983 DOI: 10.3390/children9121935] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 10/16/2022] [Revised: 11/19/2022] [Accepted: 11/25/2022] [Indexed: 12/13/2022]
Abstract
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.
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Affiliation(s)
- Vilelmine Carayanni
- School of Administration Economics and Social Sciences, Department of Tourism Administration, University of West Attica, 28 Saint Spyridonos Str., 12243 Egaleo, Greece
- Correspondence:
| | - Gregory C. Bogdanis
- School of Physical Education & Sports Science, National and Kapodistrian University of Athens, 41 Ethnikis Antistaseos Str., Daphne, 17237 Athens, Greece
| | - Elpis Vlachopapadopoulou
- Department of Endocrinology-Growth and Development, Children’s Hospital P. & A. Kyriakou, Thivon & Levadeias Str., Ampelokipoi T.K., 11527 Athens, Greece
| | - Dimitra Koutsouki
- School of Physical Education & Sports Science, National and Kapodistrian University of Athens, 41 Ethnikis Antistaseos Str., Daphne, 17237 Athens, Greece
| | - Yannis Manios
- Department of Nutrition & Dietetics, School of Health Science & Education, Harokopio University, 70 El Venizelou Ave. Kallithea, 17671 Athens, Greece
| | - Feneli Karachaliou
- Department of Endocrinology-Growth and Development, Children’s Hospital P. & A. Kyriakou, Thivon & Levadeias Str., Ampelokipoi T.K., 11527 Athens, Greece
| | - Theodora Psaltopoulou
- Department of Hygiene, Epidemiology and Medical Statistics, School of Medicine, National and Kapodistrian University of Athens, 75 Mikras Asias Str., 11527 Goudi, Greece
| | - Stefanos Michalacos
- Department of Endocrinology-Growth and Development, Children’s Hospital P. & A. Kyriakou, Thivon & Levadeias Str., Ampelokipoi T.K., 11527 Athens, Greece
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Xiang L, Deng K, Mei Q, Gao Z, Yang T, Wang A, Fernandez J, Gu Y. Population and Age-Based Cardiorespiratory Fitness Level Investigation and Automatic Prediction. Front Cardiovasc Med 2022; 8:758589. [PMID: 35071342 PMCID: PMC8767158 DOI: 10.3389/fcvm.2021.758589] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/14/2021] [Accepted: 12/07/2021] [Indexed: 01/22/2023] Open
Abstract
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.
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Affiliation(s)
- Liangliang Xiang
- Faculty of Sports Science, Ningbo University, Ningbo, China.,Research Academy of Grand Health, Ningbo University, Ningbo, China.,Auckland Bioengineering Institute, The University of Auckland, Auckland, New Zealand
| | - Kaili Deng
- Medical School, Ningbo University, Ningbo, China
| | - Qichang Mei
- Faculty of Sports Science, Ningbo University, Ningbo, China.,Research Academy of Grand Health, Ningbo University, Ningbo, China.,Auckland Bioengineering Institute, The University of Auckland, Auckland, New Zealand
| | - Zixiang Gao
- Faculty of Sports Science, Ningbo University, Ningbo, China.,Research Academy of Grand Health, Ningbo University, Ningbo, China
| | - Tao Yang
- Faculty of Sports Science, Ningbo University, Ningbo, China.,Research Academy of Grand Health, Ningbo University, Ningbo, China
| | - Alan Wang
- Auckland Bioengineering Institute, The University of Auckland, Auckland, New Zealand.,Faculty of Medicine and Health Sciences, The University of Auckland, Auckland, New Zealand
| | - Justin Fernandez
- Research Academy of Grand Health, Ningbo University, Ningbo, China.,Auckland Bioengineering Institute, The University of Auckland, Auckland, New Zealand.,Department of Engineering Science, The University of Auckland, Auckland, New Zealand
| | - Yaodong Gu
- Faculty of Sports Science, Ningbo University, Ningbo, China.,Research Academy of Grand Health, Ningbo University, Ningbo, China.,Auckland Bioengineering Institute, The University of Auckland, Auckland, New Zealand
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Recent advances in machine learning for maximal oxygen uptake (VO2 max) prediction: A review. INFORMATICS IN MEDICINE UNLOCKED 2022. [DOI: 10.1016/j.imu.2022.100863] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/18/2022] Open
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Estimated Artificial Neural Network Modeling of Maximal Oxygen Uptake Based on Multistage 10-m Shuttle Run Test in Healthy Adults. INTERNATIONAL JOURNAL OF ENVIRONMENTAL RESEARCH AND PUBLIC HEALTH 2021; 18:ijerph18168510. [PMID: 34444259 PMCID: PMC8391137 DOI: 10.3390/ijerph18168510] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 07/12/2021] [Revised: 08/11/2021] [Accepted: 08/11/2021] [Indexed: 11/17/2022]
Abstract
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.
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Gao WD, Zheng PP, Pan JW, Fang HB, Kan J, Chen Q. Prediction of VO2max based on a 3-kilometer running test for water sports athletes. J Sports Med Phys Fitness 2020; 61:542-550. [PMID: 33092333 DOI: 10.23736/s0022-4707.20.11440-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/08/2022]
Abstract
BACKGROUND No studies have reported the 3-kilometer running test (3KRT) intending to predict VO2max for water sports athletes. Therefore, the purpose of this study was to develop a new model to predict the maximal aerobic capacity (VO2max) for water sports athletes based on 3KRT. METHODS One hundred and two water sports athletes completed two sessions of experiments consisting of a maximal graded exercise test (GXT) and a 3KRT. Multiple linear regression was applied to predict VO2max value based on the performance and physiological responses of 3KRT, along with participants' anthropometric and demographic variables. The predicted residual error sum of square (PRESS) and error terms (constant error and total error) were calculated to further evaluate the predictive accuracy. RESULTS Two significant prediction models based on elapsed exercise time (T3KRT), post-exercise heart rate (PHR3KRT), body mass, and gender were proposed. One model including PHR3KRT was identified (VO2max=120.77-0.028×T3KRT [second]-0.11×PHR3KRT [bpm]-0.334×body mass [kg]+8.70×gender [1: male, 0: female]), with an adjusted R2 of 0.723. Another model excluding PHR3KRT was also identified (VO2max=103.65-0.034×T3KRT [second]-0.317×Body mass [kg] + 7.89×gender [1: male, 0: female]), with an adjusted R2 of 0.713. Both models were further validated by the result of PRESS statistics. CONCLUSIONS This endurance 3-kilometer running test accurately predicted VO2max value for water sports athletes (rowers, canoeists, and kayakers), and the model excluding PHR3KRT would be easier to use.
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Affiliation(s)
- Wei D Gao
- Zhejiang Institute of Sports Science, Hangzhou, China
| | - Pan P Zheng
- Department of Physical Education and Military Sports, Zhejiang Financial College, Hangzhou, China
| | - Jing W Pan
- Physical Education and Sports Science Academic Group, National Institute of Education, Nanyang Technological University, Singapore, Singapore
| | - Hai B Fang
- Zhejiang Institute of Sports Science, Hangzhou, China
| | - Jie Kan
- Zhejiang Institute of Sports Science, Hangzhou, China
| | - Qian Chen
- Zhejiang Institute of Sports Science, Hangzhou, China -
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VO₂FITTING: A Free and Open-Source Software for Modelling Oxygen Uptake Kinetics in Swimming and other Exercise Modalities. Sports (Basel) 2019; 7:sports7020031. [PMID: 30678373 PMCID: PMC6409559 DOI: 10.3390/sports7020031] [Citation(s) in RCA: 14] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/19/2018] [Revised: 01/18/2019] [Accepted: 01/21/2019] [Indexed: 01/15/2023] Open
Abstract
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.
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