Matabuena, MarcosVidal, Juan C.Hayes, Philip R.Saavedra-García, Miguel Á.Huelin Trillo, Fernando2020-02-102020-02-102019-08-292169-3536http://hdl.handle.net/2183/24878[Abstract]: Maximum heart rate (MHR) is widely used in the prescription and monitoring of exercise intensity, and also as a criterion for the termination of sub-maximal aerobic _tness tests in clinical populations. Traditionally, MHR is predicted from an age-based formula, usually 220-age. These formulae, however, are prone to high predictive errors that potentially could lead to inaccurately prescribed or quanti_ed training or inappropriate _tness test termination. In this paper, we used functional data analysis (FDA) to create a new method to predict MHR. It uses heart rate data gathered every 5 seconds during a low intensity, sub-maximal exercise test. FDA allows the use of all the information recorded by monitoring devices in the form of a function, reducing the amount of information needed to generalize a model, besides minimizing the curse of dimensionality. The functional data model created reduced the predictive error by more than 50% compared to current models within the literature. This new approach has important bene_ts to clinicians and practitioners when using MHR to test _tness or prescribe exercise.engAtribución 3.0 Españahttp://creativecommons.org/licenses/by/3.0/es/Maximum heart rate predictionFunctional data analysisMachine learningLow intensity sub-maximal test.Predicción de frencuencia cardíaca máximaAnálisis de datos funcionalesAprendizaje automáticoIntensidad baja prueba submáximajournal articleopen access