How to calculate a maximum heart rate correctly?

Maximum heart rate (HRmax) is usually defined as the highest heart rate achieved during maximum physical exertion and depends mainly on age, but also to a lesser extent on other parameters such as: body mass index, body composition, physical capacity, age, gender and the type of exercise test. Measurement of HRmax takes place both in cardiology and in sports during exercise testing. In many situations, it is difficult to determine the maximum heart rate during the test and it becomes necessary to estimate HRmax based on the knowledge of the above-mentioned factor. This paper also presents the methods of carrying out exercise tests and the influence of pharmacotherapy on the results obtained.Maximum heart rate (HRmax) is usually defined as the highest heart rate achieved during maximum physical exertion and depends mainly on age, but also to a lesser extent on other parameters such as: body mass index, body composition, physical capacity, age, gender and the type of exercise test. Measurement of HRmax takes place both in cardiology and in sports during exercise testing. In many situations, it is difficult to determine the maximum heart rate during the test and it becomes necessary to estimate HRmax based on the knowledge of the above-mentioned factor. This paper also presents the methods of carrying out exercise tests and the influence of pharmacotherapy on the results obtained

Jak prawidłowo wyliczyć tętno maksymalne?

Abstract: Maximum heart rate (HRmax) is usually defined as the highest heart rate achieved during maximum physical exertion and depends mainly on age, but also to a lesser extent on other parameters such as: BMI, body composition, physical capacity, age, gender and the type of exercise test. Measurement of HRmax takes place both in cardiology and in sports during exercise testing. In many situations, it is difficult to determine the maximum heart rate during the test and it becomes necessary to estimate HRmax based on the knowledge of the above-mentioned factor. This paper also presents the methods of carrying out exercise tests and the influence of pharmacotherapy on the results obtained.Tętno maksymalne (HRmax) zwykle określane jest jako najwyższe tętno osiągane podczas maksymalnego wysiłku fizycznego i jest uzależnione przede wszystkim od wieku, ale również, w mniejszym stopniu, od innych parametrów takich jak: wskaźnik masy ciała, skład ciała, wydolność fizyczna, wiek, płeć oraz rodzaj badania wysiłkowego. Dokonywanie pomiaru HRmax ma miejsce zarówno w kardiologii, jak i w sporcie podczas badań wysiłkowych. W wielu sytuacjach nie udaje się wyznaczyć tętna maksymalnego podczas badania i konieczne staje się estymowanie HRmax na podstawie znajomości wyżej wymienionych czynników mających wpływ na jego wysokość. W niniejszej pracy przedstawione zostały również sposoby przeprowadzania badań wysiłkowych oraz wpływ farmakoterapii na uzyskane wyniki

Respiratory muscle training induces additional stress and training load in well-trained triathletes—randomized controlled trial

Background: Respiratory muscle training (RMT) has been investigated in the context of improved athletic performance and pulmonary function. However, psychophysiological costs of RMT remain understudied. Voluntary isocapnic hyperpnoea (VIH) and inspiratory pressure threshold loading (IPTL) are widely applied RMT methods. The main purposes of this study were to assess whether RMT induces additional load on well-trained triathletes and determine differences in RMT-induced load between sexes and applied methods.Materials and Methods: 16 well-trained triathletes (n = 16, 56% males) underwent 6 weeks of VIH or IPTL program with progressive overload. Blood markers, subjective measures, cardiac indices, near-infrared spectroscopy indices, inspiratory muscle fatigue, and RMT-induced training load were monitored pre-, in and post-sessions. We used multiple ANOVA to investigate effects of sex, training method, and time on measured parameters.Results: There were significant interactions for acid-base balance (p = 0.04 for sex, p < 0.001 for method), partial carbon dioxide pressure (p = 0.03 for sex, p < 0.001 for method), bicarbonate (p = 0.01 for method), lactate (p < 0.001 for method), RMT-induced training load (p = 0.001 for method for single session, p = 0.03 for method per week), average heart rate (p = 0.03 for sex), maximum heart rate (p = 0.02 for sex), intercostales muscle oxygenation (p = 0.007 for testing week), and intercostales muscle oxygenation recovery (p = 0.003 for testing week and p = 0.007 for method).Conclusion: We found that RMT induced additional load in well-trained triathletes. Elicited changes in monitored variables depend on sex and training method. VIH significantly increased subjective training load measures. IPTL was associated with disbalance in blood gasometry, increase in lactate, and reports of headaches and dizziness. Both methods should be applied with consideration in high-performance settings

External validation of VO2max prediction models based on recreational and elite endurance athletes

In recent years, numerous prognostic models have been developed to predict VO2max. Nevertheless, their accuracy in endurance athletes (EA) stays mostly unvalidated. This study aimed to compare predicted VO2max (pVO2max) with directly measured VO2max by assessing the transferability of the currently available prediction models based on their R$^{2}$, calibration-in-the-large, and calibration slope. 5,260 healthy adult EA underwent a maximal exertion cardiopulmonary exercise test (CPET) (84.76% male; age 34.6±9.5 yrs.; VO2max 52.97±7.39 mL·min$^{-1}$·kg$^{-1}$, BMI 23.59±2.73 kg·m$^{-2}$). 13 models have been selected to establish pVO2max. Participants were classified into four endurance subgroups (high-, recreational-, low- trained, and “transition”) and four age subgroups (18–30, 31–45, 46–60, and ≥61 yrs.). Validation was performed according to TRIPOD guidelines. pVO2max was low-to-moderately associated with direct CPET measurements (p>0.05). Models with the highest accuracy were for males on a cycle ergometer (CE) (Kokkinos R$^{2}$ = 0.64), females on CE (Kokkinos R$^{2}$ = 0.65), males on a treadmill (TE) (Wasserman R$^{2}$ = 0.26), females on TE (Wasserman R$^{2}$ = 0.30). However, selected models underestimated pVO2max for younger and higher trained EA and overestimated for older and lower trained EA. All equations demonstrated merely moderate accuracy and should only be used as a supplemental method for physicians to estimate CRF in EA. It is necessary to derive new models on EA populations to include routinely in clinical practice and sports diagnostic

COVID-19 and athletes: Endurance sport and activity resilience study—CAESAR study

Background: The COVID-19 pandemic and imposed restrictions influenced athletic societies, although current knowledge about mild COVID-19 consequences on cardiopulmonary and physiologic parameters remains inconclusive. This study aimed to assess the impact of mild COVID-19 inflection on cardiopulmonary exercise test (CPET) performance among endurance athletes (EA) with varied fitness level.Materials and Methods: 49 EA (nmale = 43, nfemale = 6, mean age = 39.94 ± 7.80 yr, height = 178.45 cm, weight = 76.62 kg; BMI = 24.03 kgm−2) underwent double treadmill or cycle ergometer CPET and body analysis (BA) pre- and post-mild COVID-19 infection. Mild infection was defined as: (1) without hospitalization and (2) without prolonged health complications lasting for >14 days. Speed, power, heart rate (HR), oxygen uptake (VO2), pulmonary ventilation, blood lactate concentration (at the anaerobic threshold (AT)), respiratory compensation point (RCP), and maximum exertion were measured before and after COVID-19 infection. Pearson’s and Spearman’s r correlation coefficients and Student t-test were applied to assess relationship between physiologic or exercise variables and time.Results: The anthropometric measurements did not differ significantly before and after COVID-19. There was a significant reduction in VO2 at the AT and RCP (both p < 0.001). Pre-COVID-19 VO2 was 34.97 ± 6.43 ml kg·min−1, 43.88 ± 7.31 ml kg·min−1 and 47.81 ± 7.81 ml kg·min−1 respectively for AT, RCP and maximal and post-COVID-19 VO2 was 32.35 ± 5.93 ml kg·min−1, 40.49 ± 6.63 ml kg·min−1 and 44.97 ± 7.00 ml kg·min−1 respectively for AT, RCP and maximal. Differences of HR at AT (p < 0.001) and RCP (p < 0.001) was observed. The HR before infection was 145.08 ± 10.82 bpm for AT and 168.78 ± 9.01 bpm for RCP and HR after infection was 141.12 ± 9.99 bpm for AT and 165.14 ± 9.74 bpm for RCP. Time-adjusted measures showed significance for body fat (r = 0.46, p < 0.001), fat mass (r = 0.33, p = 0.020), cycling power at the AT (r = −0.29, p = 0.045), and HR at RCP (r = −0.30, p = 0.036).Conclusion: A mild COVID-19 infection resulted in a decrease in EA’s CPET performance. The most significant changes were observed for VO2 and HR. Medical Professionals and Training Specialists should be aware of the consequences of a mild COVID-19 infection in order to recommend optimal therapeutic methods and properly adjust the intensity of training

Impact of marathon performance on muscles stiffness in runners over 50 years old

IntroductionThe research examines the relationship between marathon performance and muscle stiffness changes from pre to marathon in recreational runners aged 50+ years.MethodsThirty-one male long-distance runners aged 50–73 years participated in the experiment. The muscle stiffness of quadriceps and calves was measured in two independent sessions: the day before the marathon and 30 min after the completed marathon run using a Myoton device.Results and DiscussionThe 42.195-km run was completed in 4.30,05 h ± 35.12 min, which indicates an intensity of 79.3% ± 7.1% of HRmax. The long-term, low-intensity running exercise (marathon) in older recreational runners and the low level of HRmax and VO2max showed no statistically significant changes in muscle stiffness (quadriceps and calves). There was reduced muscle stiffness (p = 0.016), but only in the triceps of the calf in the dominant (left) leg. Moreover, to optimally evaluate the marathon and adequately prepare for the performance training program, we need to consider the direct and indirect analyses of the running economy, running technique, and HRmax and VO2max variables. These variables significantly affect marathon exercise

Modeling Physiological Predictors of Running Velocity for Endurance Athletes

Background: Properly performed training is a matter of importance for endurance athletes (EA). It allows for achieving better results and safer participation. Recently, the development of machine learning methods has been observed in sports diagnostics. Velocity at anaerobic threshold (VAT), respiratory compensation point (VRCP), and maximal velocity (Vmax) are the variables closely corresponding to endurance performance. The primary aims of this study were to find the strongest predictors of VAT, VRCP, Vmax, to derive and internally validate prediction models for males (1) and females (2) under TRIPOD guidelines, and to assess their machine learning accuracy. Materials and Methods: A total of 4001 EA (nmales = 3300, nfemales = 671; age = 35.56 ± 8.12 years; BMI = 23.66 ± 2.58 kg·m−2; VO2max = 53.20 ± 7.17 mL·min−1·kg−1) underwent treadmill cardiopulmonary exercise testing (CPET) and bioimpedance body composition analysis. XGBoost was used to select running performance predictors. Multivariable linear regression was applied to build prediction models. Ten-fold cross-validation was incorporated for accuracy evaluation during internal validation. Results: Oxygen uptake, blood lactate, pulmonary ventilation, and somatic parameters (BMI, age, and body fat percentage) showed the highest impact on velocity. For VAT R2 = 0.57 (1) and 0.62 (2), derivation RMSE = 0.909 (1); 0.828 (2), validation RMSE = 0.913 (1); 0.838 (2), derivation MAE = 0.708 (1); 0.657 (2), and validation MAE = 0.710 (1); 0.665 (2). For VRCP R2 = 0.62 (1) and 0.67 (2), derivation RMSE = 1.066 (1) and 0.964 (2), validation RMSE = 1.070 (1) and 0.978 (2), derivation MAE = 0.832 (1) and 0.752 (2), validation MAE = 0.060 (1) and 0.763 (2). For Vmax R2 = 0.57 (1) and 0.65 (2), derivation RMSE = 1.202 (1) and 1.095 (2), validation RMSE = 1.205 (1) and 1.111 (2), derivation MAE = 0.943 (1) and 0.861 (2), and validation MAE = 0.944 (1) and 0.881 (2). Conclusions: The use of machine-learning methods allows for the precise determination of predictors of both submaximal and maximal running performance. Prediction models based on selected variables are characterized by high precision and high repeatability. The results can be used to personalize training and adjust the optimal therapeutic protocol in clinical settings, with a target population of EA

HR Max Prediction Based on Age, Body Composition, Fitness Level, Testing Modality and Sex in Physically Active Population

Maximal heart rate (HRmax) is associated mostly with age, but age alone explains the variance in HRmax to a limited degree and may not be adequate to predict HRmax in certain groups. The present study was carried out on 3374 healthy Caucasian, Polish men and women, clients of a sports clinic, mostly sportspeople, with a mean age of 36.57 years, body mass 74.54 kg, maximum oxygen uptake (VO2max, ml∗kg–1 ∗min–1) 50.07. Cardiopulmonary exercise tests (CPET) were carried out on treadmills or cycle ergometers to evaluate HRmax and VO2max. Linear, multiple linear, stepwise, Ridge and LASSO regression modeling were applied to establish the relationship between HRmax, age, fitness level, VO2max, body mass, age, testing modality and body mass index (BMI). Mean HRmax predictions calculated with 5 previously published formulae were evaluated in subgroups created according to all variables. HRmax was univariately explained by a 202.5–0.53∗age formula (R2 = 19.18). The weak relationship may be explained by the similar age with small standard deviation (SD). Multiple linear regression, stepwise and LASSO yielded an R2 of 0.224, while Ridge yielded R2 0.20. Previously published formulae were less precise in the more outlying groups of the studied population, overestimating HRmax in older age groups and underestimating in younger. The 202.5–0.53∗age formula developed in the present study was the best in the studied population, yielding lowest mean errors in most groups, suggesting it could be used in more active individuals. Tanaka’s formula offers the second best overall prediction, while the 220-age formula yields remarkably high mean errors of up to 9 bpm. In conclusion, adding the studied variables in multiple regression models improves the accuracy of prediction only slightly over age alone and is unlikely to be useful in clinical practice

VO2max prediction based on submaximal cardiorespiratory relationships and body composition in male runners and cyclists: a population study

Background: Oxygen uptake (VO2) is one of the most important measures of fitness and critical vital sign. Cardiopulmonary exercise testing (CPET) is a valuable method of assessing fitness in sport and clinical settings. There is a lack of large studies on athletic populations to predict VO2max using somatic or submaximal CPET variables. Thus, this study aimed to: (1) derive prediction models for maximal VO2 (VO2max) based on submaximal exercise variables at anaerobic threshold (AT) or respiratory compensation point (RCP) or only somatic and (2) internally validate provided equations. Methods: Four thousand four hundred twenty-four male endurance athletes (EA) underwent maximal symptom-limited CPET on a treadmill (n=3330) or cycle ergometer (n=1094). The cohort was randomly divided between: variables selection (nrunners = 1998; ncyclist = 656), model building (nrunners = 666; ncyclist = 219), and validation (nrunners = 666; ncyclist = 219). Random forest was used to select the most significant variables. Models were derived and internally validated with multiple linear regression. Results: Runners were 36.24±8.45 years; BMI = 23.94 ± 2.43 kg·m−2; VO2max=53.81±6.67 mL·min−1·kg−1. Cyclists were 37.33±9.13 years; BMI = 24.34 ± 2.63 kg·m−2; VO2max=51.74±7.99 mL·min−1·kg−1. VO2 at AT and RCP were the most contributing variables to exercise equations. Body mass and body fat had the highest impact on the somatic equation. Model performance for VO2max based on variables at AT was R2=0.81, at RCP was R2=0.91, at AT and RCP was R2=0.91 and for somatic-only was R2=0.43. Conclusions: Derived prediction models were highly accurate and fairly replicable. Formulae allow for precise estimation of VO2max based on submaximal exercise performance or somatic variables. Presented models are applicable for sport and clinical settling. They are a valuable supplementary method for fitness practitioners to adjust individualised training recommendations. Funding: No external funding was received for this work

Transferability of Cardiopulmonary Parameters between Treadmill and Cycle Ergometer Testing in Male Triathletes—Prediction Formulae

Cardiopulmonary exercise testing (CPET) on a treadmill (TE) or cycle ergometry (CE) is a common method in sports diagnostics to assess athletes’ aerobic fitness and prescribe training. In a triathlon, the gold standard is performing both CE and TE CPET. The purpose of this research was to create models using CPET results from one modality to predict results for the other modality. A total of 152 male triathletes (age = 38.20 ± 9.53 year; BMI = 23.97 ± 2.10 kg·m−2) underwent CPET on TE and CE, preceded by body composition (BC) analysis. Speed, power, heart rate (HR), oxygen uptake (VO2), respiratory exchange ratio (RER), ventilation (VE), respiratory frequency (fR), blood lactate concentration (LA) (at the anaerobic threshold (AT)), respiratory compensation point (RCP), and maximum exertion were measured. Random forests (RF) were used to find the variables with the highest importance, which were selected for multiple linear regression (MLR) models. Based on R2 and RF variable selection, MLR equations in full, simplified, and the most simplified forms were created for VO2AT, HRAT, VO2RCP, HRRCP, VO2max, and HRmax for CE (R2 = 0.46–0.78) and TE (R2 = 0.59–0.80). By inputting only HR and power/speed into the RF, MLR models for practical HR calculation on TE and CE (both R2 = 0.41–0.75) were created. BC had a significant impact on the majority of CPET parameters. CPET parameters can be accurately predicted between CE and TE testing. Maximal parameters are more predictable than submaximal. Only HR and speed/power from one testing modality could be used to predict HR for another. Created equations, combined with BC analysis, could be used as a method of choice in comprehensive sports diagnostics