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Cundrič L, Bosnić Z, Kaminsky LA, Myers J, Peterman JE, Markovic V, Arena R, Popović D. A Machine Learning Approach to Developing an Accurate Prediction of Maximal Heart Rate During Exercise Testing in Apparently Healthy Adults. J Cardiopulm Rehabil Prev 2023; 43:377-383. [PMID: 36880964 DOI: 10.1097/hcr.0000000000000786] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 03/08/2023]
Abstract
PURPOSE Maximal heart rate (HR max ) continues to be an important measure of adequate effort during an exercise test. The aim of this study was to improve the accuracy of HR max prediction using a machine learning (ML) approach. METHODS We used a sample from the Fitness Registry of the Importance of Exercise National Database, which included 17 325 apparently healthy individuals (81% males) who performed a maximal cardiopulmonary exercise test. Two standard formulas for HR max prediction were tested: Formula1 = 220 - age (yr), root-mean-squared error (RMSE) 21.9, relative root-mean-squared error (RRMSE) 1.1; and Formula2 = 209.3 - 0.72 × age (yr), RMSE 22.7 and RRMSE 1.1. For ML model prediction, we used age, weight, height, resting HR, and systolic and diastolic blood pressure. The following ML algorithms to predict HR max were applied: lasso regression (LR), neural networks (NN), support vector machine (SVM) and random forests (RF). An evaluation was performed using cross-validation and by computing the RMSE and RRMSE, Pearson correlation, and Bland-Altman plots. The best predictive model was explained with Shapley Additive Explanations (SHAP). RESULTS The HR max for the cohort was 162 ± 20 bpm. All ML models improved HR max prediction and reduced RMSE and RRMSE compared with Formula1 (LR: 20.2%, NN: 20.4%, SVM: 22.2%, and RF: 24.7%). The predictions of all algorithms significantly correlated with HR max ( r = 0.49, 0.51, 0.54, 0.57, respectively; P < .001). Bland-Altman analysis demonstrated lower bias and 95% CI for all ML models in comparison with standard equations. The SHAP explanation showed a high impact of all selected variables. CONCLUSIONS Machine learning, particularly the RF model, improved prediction of HR max using readily available measures. This approach should be considered for clinical application to refine HR max prediction.
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Affiliation(s)
- Larsen Cundrič
- University of Ljubljana, Faculty of Computer and Information Science, Ljubljana, Slovenia (Mr Cundrič and Dr Bosnić); Fisher Institute of Health and Well-Being and Clinical Exercise Physiology Laboratory, Ball State University, Muncie, Indiana (Drs Kaminsky and Peterman); VA Palo Alto Health Care System and Stanford University, Palo Alto, California (Dr Myers); Departments of Information Systems, Faculty of Organizational Sciences (Dr Markovic) and Physiology, Faculty of Pharmacy (Dr Popović), University of Belgrade, Belgrade, Serbia; Department of Physical Therapy, College of Applied Science, University of Illinois at Chicago (Dr Arena); Division of Cardiology, University Clinical Center of Serbia, Belgrade, Serbia (Dr Popović); and Department for Cardiovascular Diseases, Mayo Clinic, Rochester, Minnesota (Dr Popović)
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Römer C, Wolfarth B. Prediction of Relevant Training Control Parameters at Individual Anaerobic Threshold without Blood Lactate Measurement. INTERNATIONAL JOURNAL OF ENVIRONMENTAL RESEARCH AND PUBLIC HEALTH 2023; 20:4641. [PMID: 36901647 PMCID: PMC10001845 DOI: 10.3390/ijerph20054641] [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: 01/30/2023] [Revised: 02/24/2023] [Accepted: 02/28/2023] [Indexed: 06/18/2023]
Abstract
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.
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Zimatore G, Serantoni C, Gallotta MC, Guidetti L, Maulucci G, De Spirito M. Automatic Detection of Aerobic Threshold through Recurrence Quantification Analysis of Heart Rate Time Series. INTERNATIONAL JOURNAL OF ENVIRONMENTAL RESEARCH AND PUBLIC HEALTH 2023; 20:1998. [PMID: 36767364 PMCID: PMC9916349 DOI: 10.3390/ijerph20031998] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 12/19/2022] [Revised: 01/18/2023] [Accepted: 01/19/2023] [Indexed: 06/18/2023]
Abstract
During exercise with increasing intensity, the human body transforms energy with mechanisms dependent upon actual requirements. Three phases of the body's energy utilization are recognized, characterized by different metabolic processes, and separated by two threshold points, called aerobic (AerT) and anaerobic threshold (AnT). These thresholds occur at determined values of exercise intensity(workload) and can change among individuals. They are considered indicators of exercise capacities and are useful in the personalization of physical activity plans. They are usually detected by ventilatory or metabolic variables and require expensive equipment and invasive measurements. Recently, particular attention has focused on AerT, which is a parameter especially useful in the overweight and obese population to determine the best amount of exercise intensity for weight loss and increasing physical fitness. The aim of study is to propose a new procedure to automatically identify AerT using the analysis of recurrences (RQA) relying only on Heart rate time series, acquired from a cohort of young athletes during a sub-maximal incremental exercise test (Cardiopulmonary Exercise Test, CPET) on a cycle ergometer. We found that the minima of determinism, an RQA feature calculated from the Recurrence Quantification by Epochs (RQE) approach, identify the time points where generic metabolic transitions occur. Among these transitions, a criterion based on the maximum convexity of the determinism minima allows to detect the first metabolic threshold. The ordinary least products regression analysis shows that values of the oxygen consumption VO2, heart rate (HR), and Workload correspondent to the AerT estimated by RQA are strongly correlated with the one estimated by CPET (r > 0.64). Mean percentage differences are <2% for both HR and VO2 and <11% for Workload. The Technical Error for HR at AerT is <8%; intraclass correlation coefficients values are moderate (≥0.66) for all variables at AerT. This system thus represents a useful method to detect AerT relying only on heart rate time series, and once validated for different activities, in future, can be easily implemented in applications acquiring data from portable heart rate monitors.
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Affiliation(s)
- Giovanna Zimatore
- Department of Theoretical and Applied Sciences, eCampus University, 22060 Novedrate, Italy
- CNR Institute for Microelectronics and Microsystems (IMM), 40129 Bologna, Italy
| | - Cassandra Serantoni
- Neuroscience Department, Biophysics Section, Università Cattolica del Sacro Cuore, 00168 Rome, Italy
- Fondazione Policlinico Universitario A. Gemelli IRCCS, 00168 Rome, Italy
| | - Maria Chiara Gallotta
- Department of Physiology and Pharmacology “Vittorio Erspamer”, Sapienza University of Rome, 00185 Rome, Italy
| | - Laura Guidetti
- Department Unicusano, Niccolò Cusano University, 00166 Rome, Italy
| | - Giuseppe Maulucci
- Neuroscience Department, Biophysics Section, Università Cattolica del Sacro Cuore, 00168 Rome, Italy
- Fondazione Policlinico Universitario A. Gemelli IRCCS, 00168 Rome, Italy
| | - Marco De Spirito
- Neuroscience Department, Biophysics Section, Università Cattolica del Sacro Cuore, 00168 Rome, Italy
- Fondazione Policlinico Universitario A. Gemelli IRCCS, 00168 Rome, Italy
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Staffini A, Svensson T, Chung UI, Svensson AK. Heart Rate Modeling and Prediction Using Autoregressive Models and Deep Learning. SENSORS (BASEL, SWITZERLAND) 2021; 22:s22010034. [PMID: 35009581 PMCID: PMC8747593 DOI: 10.3390/s22010034] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/05/2021] [Revised: 12/20/2021] [Accepted: 12/20/2021] [Indexed: 05/04/2023]
Abstract
Physiological time series are affected by many factors, making them highly nonlinear and nonstationary. As a consequence, heart rate time series are often considered difficult to predict and handle. However, heart rate behavior can indicate underlying cardiovascular and respiratory diseases as well as mood disorders. Given the importance of accurate modeling and reliable predictions of heart rate fluctuations for the prevention and control of certain diseases, it is paramount to identify models with the best performance in such tasks. The objectives of this study were to compare the results of three different forecasting models (Autoregressive Model, Long Short-Term Memory Network, and Convolutional Long Short-Term Memory Network) trained and tested on heart rate beats per minute data obtained from twelve heterogeneous participants and to identify the architecture with the best performance in terms of modeling and forecasting heart rate behavior. Heart rate beats per minute data were collected using a wearable device over a period of 10 days from twelve different participants who were heterogeneous in age, sex, medical history, and lifestyle behaviors. The goodness of the results produced by the models was measured using both the mean absolute error and the root mean square error as error metrics. Despite the three models showing similar performance, the Autoregressive Model gave the best results in all settings examined. For example, considering one of the participants, the Autoregressive Model gave a mean absolute error of 2.069 (compared to 2.173 of the Long Short-Term Memory Network and 2.138 of the Convolutional Long Short-Term Memory Network), achieving an improvement of 5.027% and 3.335%, respectively. Similar results can be observed for the other participants. The findings of the study suggest that regardless of an individual's age, sex, and lifestyle behaviors, their heart rate largely depends on the pattern observed in the previous few minutes, suggesting that heart rate can be reasonably regarded as an autoregressive process. The findings also suggest that minute-by-minute heart rate prediction can be accurately performed using a linear model, at least in individuals without pathologies that cause heartbeat irregularities. The findings also suggest many possible applications for the Autoregressive Model, in principle in any context where minute-by-minute heart rate prediction is required (arrhythmia detection and analysis of the response to training, among others).
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Affiliation(s)
- Alessio Staffini
- Precision Health, Department of Bioengineering, Graduate School of Engineering, The University of Tokyo, Tokyo 113-8656, Japan; (A.S.); (U.-i.C.); (A.K.S.)
- Department of Economics and Finance, Catholic University of Milan, 20123 Milan, Italy
- Business Promotion Division, ALBERT Inc., Tokyo 169-0074, Japan
| | - Thomas Svensson
- Precision Health, Department of Bioengineering, Graduate School of Engineering, The University of Tokyo, Tokyo 113-8656, Japan; (A.S.); (U.-i.C.); (A.K.S.)
- School of Health Innovation, Kanagawa University of Human Services Graduate School, Yokosuka 210-0821, Japan
- Department of Clinical Sciences, Lund University, Skåne University Hospital, 221 84 Malmo, Sweden
- Correspondence:
| | - Ung-il Chung
- Precision Health, Department of Bioengineering, Graduate School of Engineering, The University of Tokyo, Tokyo 113-8656, Japan; (A.S.); (U.-i.C.); (A.K.S.)
- School of Health Innovation, Kanagawa University of Human Services Graduate School, Yokosuka 210-0821, Japan
- Clinical Biotechnology, Center for Disease Biology and Integrative Medicine, Graduate School of Medicine, The University of Tokyo, Tokyo 113-0033, Japan
| | - Akiko Kishi Svensson
- Precision Health, Department of Bioengineering, Graduate School of Engineering, The University of Tokyo, Tokyo 113-8656, Japan; (A.S.); (U.-i.C.); (A.K.S.)
- Department of Clinical Sciences, Lund University, Skåne University Hospital, 221 84 Malmo, Sweden
- Department of Diabetes and Metabolic Diseases, The University of Tokyo, Tokyo 113-8655, Japan
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Sgrò P, Emerenziani GP, Antinozzi C, Sacchetti M, Di Luigi L. Exercise as a drug for glucose management and prevention in type 2 diabetes mellitus. Curr Opin Pharmacol 2021; 59:95-102. [PMID: 34182427 DOI: 10.1016/j.coph.2021.05.006] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/25/2021] [Revised: 05/19/2021] [Accepted: 05/25/2021] [Indexed: 02/06/2023]
Abstract
Physical inactivity and sedentary behavior are risk factors for type 2 diabetes mellitus (T2DM). Therefore, physical exercise (PE) together with medical treatment might be considered as a key strategy to counteract T2DM. Glycemic control is a central objective in the prevention and management of T2DM, and PE might be able to substantially affect the processes that determine it. Just like a drug, exercise can be dosed based on the characteristics of the individual to increase its benefits and reduce side effects. In this brief review, the mechanisms underlying the effects of PE on glucose metabolism in muscle are illustrated, and the effects of modulation of the parameters characterizing this atypical "drug" on glucose homeostasis are described.
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Affiliation(s)
- Paolo Sgrò
- Department of Movement, Human and Health Sciences, University of Rome "Foro Italico", Rome, Italy
| | - Gian Pietro Emerenziani
- Department of Experimental and Clinical Medicine, University of Catanzaro "Magna Græcia", Catanzaro, Italy
| | - Cristina Antinozzi
- Department of Movement, Human and Health Sciences, University of Rome "Foro Italico", Rome, Italy
| | - Massimo Sacchetti
- Department of Movement, Human and Health Sciences, University of Rome "Foro Italico", Rome, Italy
| | - Luigi Di Luigi
- Department of Movement, Human and Health Sciences, University of Rome "Foro Italico", Rome, Italy.
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Moghetti P, Balducci S, Guidetti L, Mazzuca P, Rossi E, Schena F, Moghetti P, Balducci S, Guidetti L, Schena F, Mazzuca P, Rossi E. Walking for subjects with type 2 diabetes: a systematic review and joint AMD/SID/SISMES evidence-based practical guideline. SPORT SCIENCES FOR HEALTH 2021. [DOI: 10.1007/s11332-020-00690-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/30/2022]
Abstract
Abstract
Aims
Regular exercise is considered a cornerstone in the management of type 2 diabetes mellitus (T2DM). It improves glucose control and cardiovascular risk factors, contributes to weight loss, and also improves general well-being, likely playing a role in the prevention of chronic complications of diabetes. However, compliance to exercise recommendations is generally inadequate in subjects with T2DM. Walking is the most ancestral form of physical activity in humans, easily applicable in daily life. It may represent, in many patients, a first simple step towards lifestyle changes. Nevertheless, while most diabetic patients do not engage in any weekly walking, exercise guidelines do not generally detail how to improve its use. The aims of this document are to conduct a systematic review of available literature on walking as a therapeutic tool for people with T2DM, and to provide practical, evidence-based clinical recommendations regarding its utilization in these subjects.
Data synthesis
Analysis of available RCTs proved that regular walking training, especially when supervised, improves glucose control in subjects with T2DM, with favorable effects also on cardiorespiratory fitness, body weight, and blood pressure. Moreover, some recent studies have shown that even short bouts of walking, used for breaking prolonged sitting, can ameliorate glucose profiles in diabetic patients with sedentary behavior.
Conclusions
There is sufficient evidence to recognize that walking is a useful therapeutic tool for people with T2DM. This document discusses theoretical and practical issues for improving its use.
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Moghetti P, Balducci S, Guidetti L, Mazzuca P, Rossi E, Schena F. Walking for subjects with type 2 diabetes: A systematic review and joint AMD/SID/SISMES evidence-based practical guideline. Nutr Metab Cardiovasc Dis 2020; 30:1882-1898. [PMID: 32998820 DOI: 10.1016/j.numecd.2020.08.021] [Citation(s) in RCA: 29] [Impact Index Per Article: 7.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/14/2020] [Revised: 08/14/2020] [Accepted: 08/17/2020] [Indexed: 12/19/2022]
Abstract
AIMS Regular exercise is considered a cornerstone in the management of type 2 diabetes mellitus (T2DM). It improves glucose control and cardiovascular risk factors, contributes to weight loss, and also improves general well-being, likely playing a role in the prevention of chronic complications of diabetes. However, compliance to exercise recommendations is generally inadequate in subjects with T2DM. Walking is the most ancestral form of physical activity in humans, easily applicable in daily life. It may represent, in many patients, a first simple step towards lifestyle changes. Nevertheless, while most diabetic patients do not engage in any weekly walking, exercise guidelines do not generally detail how to improve its use. The aims of this document are to conduct a systematic review of available literature on walking as a therapeutic tool for people with T2DM, and to provide practical, evidence-based clinical recommendations regarding its utilization in these subjects. DATA SYNTHESIS Analysis of available RCTs proved that regular walking training, especially when supervised, improves glucose control in subjects with T2DM, with favorable effects also on cardiorespiratory fitness, body weight and blood pressure. Moreover, some recent studies have shown that even short bouts of walking, used for breaking prolonged sitting, can ameliorate glucose profiles in diabetic patients with sedentary behavior. CONCLUSIONS There is sufficient evidence to recognize that walking is a useful therapeutic tool for people with T2DM. This document discusses theoretical and practical issues for improving its use. This article is co-published in the journals Sport Sciences for Health and Nutrition, Metabolism and Cardiovascular Diseases.
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Affiliation(s)
- P Moghetti
- Unit of Endocrinology, Diabetes and Metabolism, Department of Medicine, University of Verona, Verona, Italy; Hospital Trust of Verona, Verona, Italy.
| | - S Balducci
- Department of Clinical and Molecular Medicine, La Sapienza University, Rome, Italy; Diabetes Unit, Sant'Andrea University Hospital, Rome, Italy; Metabolic Fitness Association, Monterotondo, Rome, Italy
| | - L Guidetti
- Department of Movement, Human and Health Sciences, University of Rome "Foro Italico", Rome, Italy
| | - P Mazzuca
- Unit of Internal Medicine, Diabetes and Metabolic Disease Center, Romagna Health District, Rimini, Italy; Department for Life Quality Studies, University of Bologna, Rimini, Italy
| | - E Rossi
- Diabetes Unit, ASL of Benevento, Benevento, Italy
| | - F Schena
- Department of Neurosciences, Biomedicine and Movement Sciences, University of Verona, Verona, Italy
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Zimatore G, Gallotta MC, Innocenti L, Bonavolontà V, Ciasca G, De Spirito M, Guidetti L, Baldari C. Recurrence quantification analysis of heart rate variability during continuous incremental exercise test in obese subjects. CHAOS (WOODBURY, N.Y.) 2020; 30:033135. [PMID: 32237785 DOI: 10.1063/1.5140455] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/28/2019] [Accepted: 02/25/2020] [Indexed: 05/20/2023]
Abstract
The present paper concerns a new description of changing in metabolism during incremental exercises test that permit an individually tailored program of exercises for obese subjects. We analyzed heart rate variability from RR interval time series (tachogram) with an alternative approach, the recurrence quantification analysis, that allows a description of a time series in terms of its dynamic structure and is able to identify the phase transitions. A transition in cardiac signal dynamics was detected and it perfectly reflects the aerobic threshold, as identified by gas exchange during an incremental exercise test, revealing the coupling from the respiratory system toward the heart. Moreover, our analysis shows that, in the recurrence plot of RR interval, it is possible to identify a specific pattern that allows to identify phase transitions between different dynamic regimes. The perfect match of the occurrence of the phase transitions with changes observed in the VO2 consumption, the gold standard approach to estimate thresholds, strongly supports the possibility of using our analysis of RR interval to detect metabolic threshold. In conclusion, we propose a novel nonlinear data analysis method that allows for an easy and personalized detection of thresholds both from professional and even from low-cost wearable devices, without the need of expensive gas analyzers.
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Affiliation(s)
- G Zimatore
- Department of Theoretical and Applied Sciences, eCampus University, Novedrate (CO) 22060, Italy
| | - M C Gallotta
- Department of Human, Movement and Health Sciences, University of Rome "Foro Italico," Rome 00135, Italy
| | - L Innocenti
- Department of Human, Movement and Health Sciences, University of Rome "Foro Italico," Rome 00135, Italy
| | - V Bonavolontà
- Department of Basic Medical Sciences, Neuroscience and Sense Organs, University of Bari "Aldo Moro," Bari 70121, Italy
| | - G Ciasca
- Institute of Physics, Fondazione Policlinico Universitario A. Gemelli IRCCS, Università Cattolica del Sacro Cuore Rome 00168, Italy
| | - M De Spirito
- Institute of Physics, Fondazione Policlinico Universitario A. Gemelli IRCCS, Università Cattolica del Sacro Cuore Rome 00168, Italy
| | - L Guidetti
- Department of Human, Movement and Health Sciences, University of Rome "Foro Italico," Rome 00135, Italy
| | - C Baldari
- Department of Theoretical and Applied Sciences, eCampus University, Novedrate (CO) 22060, Italy
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9
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Emerenziani GP, Ferrari D, Marocco C, Greco EA, Migliaccio S, Lenzi A, Baldari C, Guidetti L. Relationship between individual ventilatory threshold and maximal fat oxidation (MFO) over different obesity classes in women. PLoS One 2019; 14:e0215307. [PMID: 30973930 PMCID: PMC6459513 DOI: 10.1371/journal.pone.0215307] [Citation(s) in RCA: 12] [Impact Index Per Article: 2.4] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/12/2018] [Accepted: 03/29/2019] [Indexed: 11/26/2022] Open
Abstract
Objective The use of the Individual Ventilatory Threshold (IVT), as parameter to prescribe exercise intensity in individuals with obesity, has become more frequent during the last years. This study aimed to evaluate the relationship between IVT and Maximal Fat Oxidation (MFO) in women with obesity. Methods Fifty-two obese female adults (age = 43.6±10.9 years; BMI = 38.5±5.2 kg/m2) were included in this study. According to the BMI classification, subjects were divided into three groups: Obese Class I (OBI, n = 16); Obese Class II (OBII, n = 20) and Obese Class III (OBIII, n = 16). All subjects performed an incremental graded exercise test to evaluate peak oxygen uptake (VO2peak), IVT and MFO. MFO was evaluated using a stoichiometric equation. Fat max zone was determined for each subject within 10% of fat oxidation rates at MFO. For each HR, %HRmax, VO2 and %VO2peak variable, Pearson’s correlation test was done between IVT and MFO exercise intensity. When statistical correlation was found we used a comparative statistical analysis to assess differences between IVT and MFO. Statistical significance was set at P ≤ 0.05. Results For each HR, %HRmax, VO2 and %VO2peak variable there was a positive significant correlation (P<0.01) between IVT and MFO. No significant differences were found for HR, %HRmax, and VO2 between IVT and MFO. %VO2peak was significantly higher at IVT than at MFO (P = 0.03). MFO rates were significantly higher in OBIII women than in women of the other two classes. In all subjects, IVT was within the fat max zone. Conclusion The use of HR and VO2 corresponding to IVT could be a useful parameter not only to improve cardiorespiratory fitness but also to prescribe physical activity that maximize fat oxidation in obese subjects.
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Affiliation(s)
- Gian Pietro Emerenziani
- Department of Experimental and Clinical Medicine, University of Magna Graecia of Catanzaro, Catanzaro, Italy
| | - Dafne Ferrari
- Department of Movement, Human and Health Sciences, Section of Health Sciences, University of Rome “Foro Italico”, Rome, Italy
| | - Chiara Marocco
- Department of Movement, Human and Health Sciences, Section of Health Sciences, University of Rome “Foro Italico”, Rome, Italy
| | - Emanuela A. Greco
- Department of Experimental Medicine, Section of Medical Pathophysiology, Endocrinology and Nutrition, Sapienza University of Rome, Rome, Italy
| | - Silvia Migliaccio
- Department of Movement, Human and Health Sciences, Section of Health Sciences, University of Rome “Foro Italico”, Rome, Italy
| | - Andrea Lenzi
- Department of Experimental Medicine, Section of Medical Pathophysiology, Endocrinology and Nutrition, Sapienza University of Rome, Rome, Italy
| | | | - Laura Guidetti
- Department of Movement, Human and Health Sciences, Section of Health Sciences, University of Rome “Foro Italico”, Rome, Italy
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Emerenziani GP, Gallotta MC, Migliaccio S, Greco EA, Marocco C, di Lazzaro L, Fornari R, Lenzi A, Baldari C, Guidetti L. Response to: Comment #2 on "Differences in Ventilatory Threshold for Exercise Prescription in Outpatient Diabetic and Sarcopenic Obese Subjects". Int J Endocrinol 2018; 2018:3093208. [PMID: 30675158 PMCID: PMC6323419 DOI: 10.1155/2018/3093208] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Track Full Text] [Download PDF] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/06/2018] [Accepted: 09/13/2018] [Indexed: 01/05/2023] Open
Affiliation(s)
- Gian Pietro Emerenziani
- Department of Experimental and Clinical Medicine, University of Magna Graecia of Catanzaro, Catanzaro, Italy
| | - Maria Chiara Gallotta
- Department of Movement, Human and Health Sciences, University of Rome “Foro Italico”, Rome, Italy
| | - Silvia Migliaccio
- Department of Movement, Human and Health Sciences, University of Rome “Foro Italico”, Rome, Italy
| | - Emanuela A. Greco
- Department of Experimental Medicine, Sapienza University of Rome, Rome, Italy
- LiSa Laboratory, Policlinico of Catania, University of Catania, Catania, Italy
| | - Chiara Marocco
- Department of Experimental Medicine, Sapienza University of Rome, Rome, Italy
| | - Luca di Lazzaro
- Department of Experimental Medicine, Sapienza University of Rome, Rome, Italy
| | - Rachele Fornari
- Department of Experimental Medicine, Sapienza University of Rome, Rome, Italy
| | - Andrea Lenzi
- Department of Experimental Medicine, Sapienza University of Rome, Rome, Italy
| | | | - Laura Guidetti
- Department of Movement, Human and Health Sciences, University of Rome “Foro Italico”, Rome, Italy
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11
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Emerenziani GP, Ferrari D, Migliaccio S, Lenzi A, Greco EA, Marocco C, Baldari C, Guidetti L. Effects of body weight loss program on parameters of muscle performance in female obese adults. J Sports Med Phys Fitness 2018; 59:624-631. [PMID: 30024124 DOI: 10.23736/s0022-4707.18.08578-x] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/08/2022]
Abstract
BACKGROUND Body weight loss program may lead to a decrease in lean body mass affecting negatively muscle performance in obese subjects. Thus, the aim of this study was to examine the effect of weight loss on muscle performance in female obese subjects. METHODS Eighty obese female adults were enrolled for a 2-month unsupervised aerobic training (UAT) plus nutritional program. In the pre- and postintervention body composition was evaluated by hand-to-foot bioelectrical impedance method, body strength using handgrip test, and lower muscle power was assessed by a 5-repetition chair stand test (CST) and 30-s chair stand test (30sCST) wearing a dynamometer. RESULTS Thirty-six subjects completed the protocol, 39% had high compliance (HC), while 61% had low compliance (LC) to exercise prescription. HC group showed a significant decreased body weight, percent of fat mass and lean muscle mass after training. Both groups significantly increased CST performance while only HC significantly increased 30sCST. No differences were found in CST muscle power in both groups between pre- and post-training. However, evaluation of muscle power during 30sCST showed significantly higher value in HC group than LC group after training. CONCLUSIONS The results of our study show that although total lean muscle mass decreased after UAT, lower body muscle efficiency increased while muscle power did not change suggesting that in obese patients UAT can help to optimize weight loss and body efficiency. The data might be helpful for exercise professionals to evaluate correctly the muscle performance in obese adults after weight loss programs.
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Affiliation(s)
- Gian Pietro Emerenziani
- Department of Experimental and Clinical Medicine, "Magna Græcia" University of Catanzaro, Catanzaro, Italy
| | - Dafne Ferrari
- Section of Health Sciences, Department of Movement, Human and Health Sciences, University of Rome "Foro Italico", Rome, Italy
| | - Silvia Migliaccio
- Section of Health Sciences, Department of Movement, Human and Health Sciences, University of Rome "Foro Italico", Rome, Italy
| | - Andrea Lenzi
- Section of Medical Pathophysiology, Food Science and Endocrinology, Department of Experimental Medicine, Sapienza University, Rome, Italy
| | - Emanuela A Greco
- Section of Medical Pathophysiology, Food Science and Endocrinology, Department of Experimental Medicine, Sapienza University, Rome, Italy
| | - Chiara Marocco
- Section of Health Sciences, Department of Movement, Human and Health Sciences, University of Rome "Foro Italico", Rome, Italy
| | | | - Laura Guidetti
- Section of Health Sciences, Department of Movement, Human and Health Sciences, University of Rome "Foro Italico", Rome, Italy
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