Exercise Intensity Forecasting: Application in Elderly Interventions that Promote Active and Healthy Aging

Exercise Intensity Forecasting: Application in Elderly Interventions that Promote Active and Healthy Aging

Antonis S. Billis (Lab of Medical Physics, Aristotle University of Thessaloniki, Thessaloniki, Greece), Evdokimos I. Konstantinidis (Lab of Medical Physics, Aristotle University of Thessaloniki, Thessaloniki, Greece), Ioanna-Maria Spyrou (Lab of Medical Physics, Aristotle University of Thessaloniki, Thessaloniki, Greece), Panagiotis Antoniou (Lab of Medical Physics, Aristotle University of Thessaloniki, Thessaloniki, Greece) and Panagiotis D. Bamidis (Lab of Medical Physics, Aristotle University of Thessaloniki, Thessaloniki, Greece)
Copyright: © 2015 |Pages: 19
DOI: 10.4018/IJEHMC.2015100101
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Abstract

Heart rate monitoring in physical exercise regimens is the key indicator of the workout intensity level. Day-to-day exercise variation of the heart rate reflects any progress achieved by the trainee and helps the trainer or the trainee himself to adjust the exercise work plan accordingly. However, timely decision upon changing intensity level of exercise is of crucial importance so as to maximize the health outcomes. Prediction of future heart rate values based on the trainee's history profile may prove to be a useful decision making tool in that case. The minimum set of available heart rate measurements in combination with the existence of outliers pose restrictions so to achieve reliable predictions. Time-series forecasting state-of-the-art algorithms such as Support Vector Regression and Gaussian Processes have been used in order to extract the best forecaster for these data. Heart rate data during and at the end of an exergaming intervention of 90 seniors were analyzed and compared in different cases. No single method outperformed the others. However, forecasting error was considered acceptable and all algorithms proved to be robust enough, even in the presence of outliers and irrespective the forecasting horizon, be it short or long term.
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Introduction

In general, a training impulse intends to result in a positive training adaptation and improved physical performance. However, it is known that excessive training may lead to reduced performance as well as to the development of overtraining syndrome (Jeukendrup, Hesselink, Snyder, Kuipers, & Keizer, 1992; Lehmann, Dickhuth, Gendrisch, Lazar, Thum, Kaminski, Aramendi, Peterke, Wieland, & Keul, 1992; Lehmann, Dickhuth, Gendrisch, Lazar, Thum, Kaminski, Aramendi, Peterke, Wieland, & Keul, 1991). Often there is a fine distinction between optimal training impulses and a training impulse that deteriorates performance. Hence, it is believed that it is important to carefully monitor three components of a training program: duration, frequency and intensity. Training duration and frequency are not difficult to monitor. Regarding intensity, there are many methods for measuring it (A. Jeukendrup & VanDiemen 1998). For the optimal way to monitor intensity of exercise, a balance needs to be struck between the validity of the measured parameter and the ease of that parameter’s measurement process.

It is common knowledge that heart rate is correlated with physical exercise both in short term, as heart rate variability (HRV) i.e. the speed with which the heart rate adapts to the energy needs of the body, as well as, in long term, i.e. mean heart rate (HR). (Achten & Jeukendrup, 2003). It is also established that HR and oxygen volume (VO2 – a direct measure of energy expenditure) are linearly related over a wide range of submaximal exercise intensities. By determining the quantitative relationship between HR and VO2, HR can then be utilized to estimate VO2, which gives a fair reflection of the intensity of work that is being performed. With the development of portable, wireless Heart Rate Monitors (HRMs), HR has become the most commonly used method to get an indication of the exercise intensity in the field. HR is easy to monitor and shows a very stable pattern during exercise. As a consequence, HR data aid athletes to immediately adjust the intensity of a workout when and if necessary (Achten & Jeukendrup, 2003)

These conclusions have been around for quite some time. For that reason they have been utilized in formal guidelines and standards. In a recent American College of Sports Medicine position, a general classification of physical activity intensity was given. Utilising the percentage of HR- reserve (HRmax – HRrest) and the percentage of the HRmax the intensity of exercise is expressed. That intensity was divided into six different categories ranging from very light to maximal. This classification makes it possible to estimate the intensity of an exercise bout expressed as % VO2max or metabolic equivalents, without determining the individual relationship between HR and VO2. It is important to note that the intensity obtained this way will only give an indication of the true intensity and the individual relationship between HR and VO2 needs to be determined for a more accurate estimation. (Achten & Jeukendrup 2003; American College of Sports Medicine and others 2013).

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