Dataset Information

Validity of the Maximal Heart Rate Prediction Models among Runners and Cyclists.

ABSTRACT: Maximal heart rate (HRmax) is a widely used measure of cardiorespiratory fitness. Prediction of HRmax is an alternative to cardiopulmonary exercise testing (CPET), but its accuracy among endurance athletes (EA) requires evaluation. This study aimed to externally validate HRmax prediction models in the EA independently for running and cycling CPET. A total of 4043 runners (age = 33.6 (8.1) years; 83.5% males; BMI = 23.7 (2.5) kg·m^-2) and 1026 cyclists (age = 36.9 (9.0) years; 89.7% males; BMI = 24.0 (2.7) kg·m^-2) underwent maximum CPET. Student t-test, mean absolute percentage error (MAPE), and root mean square error (RMSE) were applied to validate eight running and five cycling HRmax equations externally. HRmax was 184.6 (9.8) beats·min^-1 and 182.7 (10.3) beats·min^-1, respectively, for running and cycling, p = 0.001. Measured and predicted HRmax differed significantly (p = 0.001) for 9 of 13 (69.2%) models. HRmax was overestimated by eight (61.5%) and underestimated by five (38.5%) formulae. Overestimated HRmax amounted to 4.9 beats·min^-1 and underestimated HRmax was in the range up to 4.9 beats·min^-1. RMSE was 9.1-10.5. MAPE ranged to 4.7%. Prediction models allow for limited precision of HRmax estimation and present inaccuracies. HRmax was more often underestimated than overestimated. Predicted HRmax can be implemented for EA as a supplemental method, but CPET is the preferable method.

SUBMITTER: Kasiak PS

PROVIDER: S-EPMC10146295 | biostudies-literature | 2023 Apr

REPOSITORIES: biostudies-literature

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Validity of the Maximal Heart Rate Prediction Models among Runners and Cyclists.

Kasiak Przemysław Seweryn PS Wiecha Szczepan S Cieśliński Igor I Takken Tim T Lach Jacek J Lewandowski Marcin M Barylski Marcin M Mamcarz Artur A Śliż Daniel D

Journal of clinical medicine 20230414 8

Maximal heart rate (HRmax) is a widely used measure of cardiorespiratory fitness. Prediction of HRmax is an alternative to cardiopulmonary exercise testing (CPET), but its accuracy among endurance athletes (EA) requires evaluation. This study aimed to externally validate HRmax prediction models in the EA independently for running and cycling CPET. A total of 4043 runners (age = 33.6 (8.1) years; 83.5% males; BMI = 23.7 (2.5) kg·m<sup>-2</sup>) and 1026 cyclists (age = 36.9 (9.0) years; 89.7% mal ...[more]

PMID: 37109218

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Project description:BackgroundThe prediction of maximal heart rate (MHR) and anaerobic threshold heart rate (HRAT) in patients with coronary heart disease (CHD), particularly among the Chinese population, remains a significant challenge. Existing equations for MHR prediction are primarily designed for healthy individuals not on medication for optimized β-blocker (BB) therapy, showing limited efficacy for individuals on various drug regimens. Moreover, the prediction of HRAT lacks established formulas. This study aims to develop equations for MHR and HRAT, assess the accuracy of historical MHR formulas, and examine their correlation with HR measurements at the anaerobic threshold (AT).MethodsAmong 2021 to 2023, 170 CHD patients were recruited. Patients were categorized into groups based on BB usage. BB dose was transformed into carvedilol dose. Multiple linear stepwise regression analysis was employed to identify predictors of MHR and HRAT, incorporating key patient variables according to prior studies (age, sex, height, weight, carvedilol dose, HRrest). The mean absolute percentage errors (MAPEs) were calculated and compared among abovementioned MHR and HRAT prediction formulas. Besides, the percentages of MHR in predicting HRAT among different formulas were calculated.ResultsFor the patients with BB medication, the simplified equations derived for MHR and HRAT were 176 - 1.2*age + 0.7*HRrest - 0.4*weight and 98 - 0.6*age + 0.7*HRrest - 0.3*weight, respectively. For those without BB medication, the derived equations for MHR and HRAT were 200 - 1.1*age and 91 - 0.5*age + 0.5*HRrest, respectively. There are significant differences between the results predicted by the new formula and the prior formulas. The new formulas are helpful for predicting the MHR of patients during exercise more accurately and guiding exercise training more scientifically.ConclusionsThe new equations for estimating MHR and HRAT in CHD patients enhance the accuracy of prior formulas. Given the BB impact on sympathetic nerve activity, the predictive formulas for MHR and HRAT were significantly improved.

Project description:BackgroundResting heart rate (HR) and HR variability (HRV) are widely used parameters to assess cardiac autonomic nervous system function noninvasively. While resting assessments can be performed during sleep or after awakening, it would be relevant to know how interchangeable the results of these measurements are. This study aimed at examining the alignment between nocturnal and morning assessments during regular endurance training and in response to intensive training. A total of 24 recreational runners performed a 3-week baseline period (BL) and a 2-week overload (OL) period (Lucia's training impulse + 80%). Their running performance was assessed with a 3000-m running test after the BL and OL. The participants recorded daily their nocturnal HR and HRV (the natural logarithm of the root mean square of successive differences; LnRMSSD) with a photoplethysmography-based wrist device and performed an orthostatic test (2-min supine, 2-min standing) every morning with a chest-strap HR sensor. The HR and LnRMSSD segments that were analyzed from the nocturnal recordings included start value (SleepStart), end value (SleepEnd), first 4-h segment 30 min after detected sleep onset (Sleep4h), and full sleep time (SleepFull). The morning segments consisted of the last-minute average in both body positions. All segments were compared at BL and in response to the 3000-m test and OL.ResultsAll nocturnal HR and LnRMSSD segments correlated with supine and standing segments at BL (r = 0.42 to 0.91, p < 0.05). After the 3000-m test, the HR increased and LnRMSSD decreased only in the SleepStart, Sleep4h, and SleepFull segments (p < 0.05). In response to the OL, the standing HR decreased (p < 0.01), while the LnRMSSD increased (p < 0.05) in all segments except for SleepStart. The Pearson correlations between relative changes in nocturnal and morning segments were - 0.11 to 0.72 (3000-m) and - 0.25 to 0.79 (OL). The OL response in Sleep4h HR and LnRMSSD correlated with the relative change in 3000-m time (r = 0.63, p = 0.001 and r=-0.50, p = 0.013, respectively).ConclusionsNocturnal and morning HR and LnRMSSD correlated moderately or highly in the majority of cases during the BL, but their responses to intensive training were not similarly aligned, especially in LnRMSSD. The nocturnal segments seemed to be sensitive to physical loading, and their responses were associated with the performance-related training responses.

Project description: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).

Project description:BackgroundOxygen 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.MethodsFour 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.ResultsRunners 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.ConclusionsDerived 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.FundingNo external funding was received for this work.

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Validity of the Maximal Heart Rate Prediction Models among Runners and Cyclists.

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Validity of the Maximal Heart Rate Prediction Models among Runners and Cyclists.

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