Maximal heart rate in young adults: A fixed 188 bpm outperforms values predicted by a classical age-based equation

Araújo, Cláudio Gil Soares de; Duarte, Carlos Vieira

doi:10.1016/j.ijcard.2015.02.043

Cited by 3 publications

(2 citation statements)

References 5 publications

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“…Maximum HR, measured by laboratory and field tests, or estimated by predictive equations, tend to be linearly attenuated with ageing, 6 especially after the age of 35 years. 7 There are many predictive equations for maximum HR in aging. The equation: maximum HR = 208 -0.7 x age (years) was validated in a Brazilian population.…”

Section: Introductionmentioning

confidence: 99%

Measuring Heart Rate During Exercise: From Artery Palpation to Monitors and Apps

Almeida¹,

Bottino²,

Ramos³

et al. 2019

International Journal of Cardiovascular Sciences

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Section: Introductionmentioning

confidence: 99%

Measuring Heart Rate During Exercise: From Artery Palpation to Monitors and Apps

Almeida¹,

Bottino²,

Ramos³

et al. 2019

International Journal of Cardiovascular Sciences

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“…HR monitors and wearable fitness devices are becoming increasingly popular and the data collected from these devices can potentially be used to make predictions of physiological parameters like VO 2 [50]. A variety of estimation methods for VO 2 or VO 2 max have been developed, but many of them rely on assumptions about the linearity of the relationship between HR and VO 2 or agebased predictions of maximum HR [2,25,35,38,44,53]. Recently, more complex mathematical modeling and machine learning techniques have been explored as a way to provide more accurate estimations of VO 2 because they are less dependent on such assumptions [2-5, 9-11, 31, 32, 38, 46].…”

Section: Introductionmentioning

confidence: 99%

Predicting oxygen uptake responses during cycling at varied intensities using an artificial neural network

Borror

Mazzoleni

Coppock

et al. 2019

Biomedical Human Kinetics

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SummaryStudy aim: Oxygen Uptake (VO2) is avaluable metric for the prescription of exercise intensity and the monitoring of training progress. However, VO2 is difficult to assess in anon-laboratory setting. Recently, an artificial neural network (ANN) was used to predict VO2 responses during aset walking protocol on the treadmill [9]. The purpose of the present study was to test the ability of an ANN to predict VO2 responses during cycling at self-selected intensities using Heart Rate (HR), time derivative of HR, power output, cadence, and body mass data.Material and methods: 12 moderately-active adult males (age: 21.1 ± 2.5 years) performed a50-minute bout of cycling at a variety of exercise intensities. VO2, HR, power output, and cadence were recorded throughout the test. An ANN was trained, validated and tested using the following inputs: HR, time derivative of HR, power output, cadence, and body mass. A twelve-fold hold-out cross validation was conducted to determine the accuracy of the model.Results: The ANN accurately predicted the experimental VO2 values throughout the test (R2 = 0.91 ± 0.04, SEE = 3.34 ± 1.07 mL/kg/min).Discussion: This preliminary study demonstrates the potential for using an ANN to predict VO2 responses during cycling at varied intensities using easily accessible inputs. The predictive accuracy is promising, especially considering the large range of intensities and long duration of exercise. Expansion of these methods could allow ageneral algorithm to be developed for a more diverse population, improving the feasibility of oxygen uptake assessment.

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A dynamical systems approach for the submaximal prediction of maximum heart rate and maximal oxygen uptake

et al. 2017

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Maximal heart rate in young adults: A fixed 188 bpm outperforms values predicted by a classical age-based equation

Cited by 3 publications

References 5 publications

Measuring Heart Rate During Exercise: From Artery Palpation to Monitors and Apps

Measuring Heart Rate During Exercise: From Artery Palpation to Monitors and Apps

Predicting oxygen uptake responses during cycling at varied intensities using an artificial neural network

A dynamical systems approach for the submaximal prediction of maximum heart rate and maximal oxygen uptake

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