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Schumacher BT, LaMonte MJ, LaCroix AZ, Simonsick EM, Hooker SP, Parada H, Bellettiere J, Kumar A. Development, validation, and transportability of several machine-learned, non-exercise-based VO 2max prediction models for older adults. JOURNAL OF SPORT AND HEALTH SCIENCE 2024; 13:611-620. [PMID: 38428731 PMCID: PMC11282333 DOI: 10.1016/j.jshs.2024.02.004] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/01/2023] [Revised: 11/30/2023] [Accepted: 01/18/2024] [Indexed: 03/03/2024]
Abstract
BACKGROUND There exist few maximal oxygen uptake (VO2max) non-exercise-based prediction equations, fewer using machine learning (ML), and none specifically for older adults. Since direct measurement of VO2max is infeasible in large epidemiologic cohort studies, we sought to develop, validate, compare, and assess the transportability of several ML VO2max prediction algorithms. METHODS The Baltimore Longitudinal Study of Aging (BLSA) participants with valid VO2max tests were included (n = 1080). Least absolute shrinkage and selection operator, linear- and tree-boosted extreme gradient boosting, random forest, and support vector machine (SVM) algorithms were trained to predict VO2max values. We developed these algorithms for: (a) the overall BLSA, (b) by sex, (c) using all BLSA variables, and (d) variables common in aging cohorts. Finally, we quantified the associations between measured and predicted VO2max and mortality. RESULTS The age was 69.0 ± 10.4 years (mean ± SD) and the measured VO2max was 21.6 ± 5.9 mL/kg/min. Least absolute shrinkage and selection operator, linear- and tree-boosted extreme gradient boosting, random forest, and support vector machine yielded root mean squared errors of 3.4 mL/kg/min, 3.6 mL/kg/min, 3.4 mL/kg/min, 3.6 mL/kg/min, and 3.5 mL/kg/min, respectively. Incremental quartiles of measured VO2max showed an inverse gradient in mortality risk. Predicted VO2max variables yielded similar effect estimates but were not robust to adjustment. CONCLUSION Measured VO2max is a strong predictor of mortality. Using ML can improve the accuracy of prediction as compared to simpler approaches but estimates of association with mortality remain sensitive to adjustment. Future studies should seek to reproduce these results so that VO2max, an important vital sign, can be more broadly studied as a modifiable target for promoting functional resiliency and healthy aging.
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Affiliation(s)
- Benjamin T Schumacher
- Herbert Wertheim School of Public Health and Human Longevity Science, University of California San Diego, La Jolla, CA 92093, USA.
| | - Michael J LaMonte
- Department of Epidemiology and Environmental Health, School of Public Health and Health Professions, University at Buffalo-State University of New York, Buffalo, NY 14214, USA
| | - Andrea Z LaCroix
- Herbert Wertheim School of Public Health and Human Longevity Science, University of California San Diego, La Jolla, CA 92093, USA
| | - Eleanor M Simonsick
- Translational Gerontology Branch, Intramural Research Program, National Institute on Aging, National Institutes of Health, Baltimore, MD 21225, USA
| | - Steven P Hooker
- College of Health and Human Services, San Diego State University, San Diego, CA 92182, USA
| | - Humberto Parada
- Division of Epidemiology and Biostatistics, School of Public Health, San Diego State University, San Diego, CA 92182, USA; University of California San Diego Moores Cancer Center, La Jolla, CA 92093, USA
| | - John Bellettiere
- Herbert Wertheim School of Public Health and Human Longevity Science, University of California San Diego, La Jolla, CA 92093, USA
| | - Arun Kumar
- Computer Science and Engineering and Halicioglu Data Science Institute, University of California San Diego, La Jolla, CA 92093, USA
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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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Rosoł M, Petelczyc M, Gąsior JS, Młyńczak M. Prediction of peak oxygen consumption using cardiorespiratory parameters from warmup and submaximal stage of treadmill cardiopulmonary exercise test. PLoS One 2024; 19:e0291706. [PMID: 38198496 PMCID: PMC10781163 DOI: 10.1371/journal.pone.0291706] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/04/2023] [Accepted: 12/19/2023] [Indexed: 01/12/2024] Open
Abstract
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.
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Affiliation(s)
- Maciej Rosoł
- Faculty of Mechatronics, Institute of Metrology and Biomedical Engineering, Warsaw University of Technology, Warsaw, Poland
| | - Monika Petelczyc
- Faculty of Physics, Warsaw University of Technology, Warsaw, Poland
| | - Jakub S. Gąsior
- Department of Pediatric Cardiology and General Pediatrics, Medical University of Warsaw, Warsaw, Poland
| | - Marcel Młyńczak
- Faculty of Mechatronics, Institute of Metrology and Biomedical Engineering, Warsaw University of Technology, Warsaw, Poland
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Neshitov A, Tyapochkin K, Kovaleva M, Dreneva A, Surkova E, Smorodnikova E, Pravdin P. Estimation of cardiorespiratory fitness using heart rate and step count data. Sci Rep 2023; 13:15808. [PMID: 37737296 PMCID: PMC10517160 DOI: 10.1038/s41598-023-43024-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/04/2022] [Accepted: 09/18/2023] [Indexed: 09/23/2023] Open
Abstract
Predicting cardiorespiratory fitness levels can be useful for measuring progress in an exercise program as well as for stratifying cardiovascular risk in asymptomatic adults. This study proposes a model to predict fitness level in terms of maximal oxygen uptake using anthropometric, heart rate, and step count data. The model was trained on a diverse cohort of 3115 healthy subjects (1035 women and 2080 men) aged 42 ± 10.6 years and tested on a cohort of 779 healthy subjects (260 women and 519 men) aged 42 ± 10.18 years. The developed model is capable of making accurate and reliable predictions with the average test set error of 3.946 ml/kg/min. The maximal oxygen uptake labels were obtained using wearable devices (Apple Watch and Garmin) during recorded workout sessions. Additionally, the model was validated on a sample of 10 subjects with maximal oxygen uptake determined directly using a treadmill protocol in a laboratory setting and showed an error of 4.982 ml/kg/min. Unlike most other models, which use accelerometer readings as additional input data, the proposed model relies solely on heart rate and step counts-data readily available on the majority of fitness trackers. The proposed model provides a point estimation and a probabilistic prediction of cardiorespiratory fitness level, thus it can estimate the prediction's uncertainty and construct confidence intervals.
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Affiliation(s)
| | | | - Marina Kovaleva
- Welltory Inc., 541 Jefferson, Suite 100, Redwood City, CA, 94063, USA
| | - Anna Dreneva
- Welltory Inc., 541 Jefferson, Suite 100, Redwood City, CA, 94063, USA
| | - Ekaterina Surkova
- Welltory Inc., 541 Jefferson, Suite 100, Redwood City, CA, 94063, USA
| | | | - Pavel Pravdin
- Welltory Inc., 541 Jefferson, Suite 100, Redwood City, CA, 94063, USA
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Dindorf C, Bartaguiz E, Gassmann F, Fröhlich M. Conceptual Structure and Current Trends in Artificial Intelligence, Machine Learning, and Deep Learning Research in Sports: A Bibliometric Review. INTERNATIONAL JOURNAL OF ENVIRONMENTAL RESEARCH AND PUBLIC HEALTH 2022; 20:173. [PMID: 36612493 PMCID: PMC9819320 DOI: 10.3390/ijerph20010173] [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: 11/26/2022] [Revised: 12/16/2022] [Accepted: 12/20/2022] [Indexed: 06/17/2023]
Abstract
Artificial intelligence and its subcategories of machine learning and deep learning are gaining increasing importance and attention in the context of sports research. This has also meant that the number of corresponding publications has become complex and unmanageably large in human terms. In the current state of the research field, there is a lack of bibliometric analysis, which would prove useful for obtaining insights into the large amounts of available literature. Therefore, the present work aims to identify important research issues, elucidate the conceptual structure of the research field, and unpack the evolutionary trends and the direction of hot topics regarding key themes in the research field of artificial intelligence in sports. Using the Scopus database, 1215 documents (reviews and articles) were selected. Bibliometric analysis was performed using VOSviewer and bibliometrix R package. The main findings are as follows: (a) the literature and research interest concerning AI and its subcategories is growing exponentially; (b) the top 20 most cited works comprise 32.52% of the total citations; (c) the top 10 journals are responsible for 28.64% of all published documents; (d) strong collaborative relationships are present, along with small, isolated collaboration networks of individual institutions; (e) the three most productive countries are China, the USA, and Germany; (f) different research themes can be characterized using author keywords with current trend topics, e.g., in the fields of biomechanics, injury prevention or prediction, new algorithms, and learning approaches. AI research activities in the fields of sports pedagogy, sports sociology, and sports economics seem to have played a subordinate role thus far. Overall, the findings of this study expand knowledge on the research situation as well as the development of research topics regarding the use of artificial intelligence in sports, and may guide researchers to identify currently relevant topics and gaps in the research.
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Affiliation(s)
- Carlo Dindorf
- Department of Sports Science, Rheinland-Pfälzische Technische Universität Kaiserslautern-Landau (RPTU), 67663 Kaiserslautern, Germany
| | - Eva Bartaguiz
- Department of Sports Science, Rheinland-Pfälzische Technische Universität Kaiserslautern-Landau (RPTU), 67663 Kaiserslautern, Germany
| | - Freya Gassmann
- Department of Empirical Social Research, Rheinland-Pfälzische Technische Universität Kaiserslautern-Landau (RPTU), 67663 Kaiserslautern, Germany
| | - Michael Fröhlich
- Department of Sports Science, Rheinland-Pfälzische Technische Universität Kaiserslautern-Landau (RPTU), 67663 Kaiserslautern, Germany
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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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