PURPOSE: Energy expenditure measured in METs is widely used in cardiovascular medicine, exercise physiology, and nutrition assessment. However, measurement of METs requires complex equipment to determine oxygen uptake. A simple method to predict oxygen uptake on the basis of HR measurements without requirement for gas analysis, movement-recording devices, or exercise equipment (treadmills, cycle ergometers) would enable a simple prediction of energy expenditure. The purpose of this study was to determine whether HR can be used to accurately predict oxygen uptake. METHODS: Published studies that reported a measured resting HR (HR(rest)), a measured activity HR (HR(absolute)), and a measured oxygen uptake (mL O(2)·kg(-1)·min(-1)) associated with the HR(absolute) were identified. A total of 220 data sets were extracted from 60 published exercise studies (total subject cohort = 11,257) involving a diverse range of age, pathophysiology, and the presence/absence of β-blocker therapy. Net HR (HR(net) = HR(absolute) - HR(rest)) and HR index (HR(index) = HR(absolute)/HR(rest)) were calculated from the HR data. A regression analysis of oxygen uptake (expressed as METs) was performed against HR(absolute), HR(net), and HR(index). RESULTS: Statistical models for the relationship between METs and the different HR parameters (HR(absolute), HR(net), and HR(index)) were developed. A comparison between regression analyses for the models and the actual data extracted from the published studies demonstrated that the best fit model was the regression equation describing the relationship between HR(index) and METs. Subgroup analyses of clinical state (normal, pathology), testing device (cycle ergometer, treadmill), test protocol (maximal, submaximal), gender, and the effect of β-blockade were all consistent with combined data analysis, demonstrating the robustness of the equation. CONCLUSIONS: HR(index) can be used to predict energy expenditure with the equation METs = 6HR(index) - 5.
PURPOSE: Energy expenditure measured in METs is widely used in cardiovascular medicine, exercise physiology, and nutrition assessment. However, measurement of METs requires complex equipment to determine oxygen uptake. A simple method to predict oxygen uptake on the basis of HR measurements without requirement for gas analysis, movement-recording devices, or exercise equipment (treadmills, cycle ergometers) would enable a simple prediction of energy expenditure. The purpose of this study was to determine whether HR can be used to accurately predict oxygen uptake. METHODS: Published studies that reported a measured resting HR (HR(rest)), a measured activity HR (HR(absolute)), and a measured oxygen uptake (mL O(2)·kg(-1)·min(-1)) associated with the HR(absolute) were identified. A total of 220 data sets were extracted from 60 published exercise studies (total subject cohort = 11,257) involving a diverse range of age, pathophysiology, and the presence/absence of β-blocker therapy. Net HR (HR(net) = HR(absolute) - HR(rest)) and HR index (HR(index) = HR(absolute)/HR(rest)) were calculated from the HR data. A regression analysis of oxygen uptake (expressed as METs) was performed against HR(absolute), HR(net), and HR(index). RESULTS: Statistical models for the relationship between METs and the different HR parameters (HR(absolute), HR(net), and HR(index)) were developed. A comparison between regression analyses for the models and the actual data extracted from the published studies demonstrated that the best fit model was the regression equation describing the relationship between HR(index) and METs. Subgroup analyses of clinical state (normal, pathology), testing device (cycle ergometer, treadmill), test protocol (maximal, submaximal), gender, and the effect of β-blockade were all consistent with combined data analysis, demonstrating the robustness of the equation. CONCLUSIONS: HR(index) can be used to predict energy expenditure with the equation METs = 6HR(index) - 5.
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