Running power meters and theoretical models based on laws of physics: Effects of environments and running conditions
Introduction
Power output (PW) is a variable widely used for training prescription and load monitoring [1]. Due to the large advantages of this parameter, new emerging technologies and devices have been developed in order to measure PW in different sports, such as cycling or running. In cycling, PW can be measured by registering directly the force applied in different parts of the bike [2]. In running activities, new emerging devices use information from the global satellite system (outdoor conditions), or from inertial sensors like accelerometers and gyroscopes (indoor conditions), for estimating the PW and other kinematics parameters [3], [4], [5].
Despite the fact that these running devices have led to an important advance for daily training [1,6], the accuracy of their data is still questioned. This accuracy is commonly analyzed by comparing the level of agreement between the PW given by a new device and a tool which is taken as the gold standard or criterion reference [7,8]. However, unlike in cycling, where the SRM® power meter has been widely used as a reference device [9,10], a gold standard for analyzing PW estimated during running activities is not easily available. Different studies have used tools such as the OptoGait system [4] or a 3D motion capture system [11] for validating kinematic parameters estimated by these commercial devices, such as the contact and flight time or the stride’ length and frequency [4,11]. Other recent studies [12] have examined the repeatability and concurrent validity of some of these running power meters, but to the best of our knowledge, no study has yet analyzed the agreement between running PW estimated by these new emerging technologies and a reference criterion.
PW in running activities can be influenced by factors such as the speed, body weight and height, air and climb resistance [13,14]. Due to the difficulty in analyzing all these factors using force platforms (commonly used as gold standard), several theoretical power outputs (TPW) based on mathematical models have emerged in the last decade [15,16]. Nevertheless, none of them have completely modeled all the aforementioned running conditions. On this matter, the Dijk and Megen [13] model (TPW1), as well as the proposal of Skiba [14] (TPW2), have been suggested in order to try to solve these limitations. Both models, based on laws of physics, offer a theoretical PW estimation by modulating the main key factors which can affect this variable: the speed, body weight, and slope. Therefore, these TPW approaches would offer two interesting criteria to analyze the effects of environment and running conditions on PW offered by new emerging devices.
Therefore, taking into account the scarcity of scientific literature analyzing the feasibility of the PW estimated by the running devices, together with the increasing commercialization of these tools, this study aimed to analyze the level of agreement between PW estimated by five commercial technologies and two theoretical models based on laws of physics, in different environments and running conditions.
Section snippets
Experimental design
Participants conducted three testing sessions. In the first session, subjects were familiarized with the methodology used in the current study, and they underwent a complete medical examination (including electrocardiogram) to ensure that all participants were able to carry out the test proposed in this study. On the second day (indoor session), subjects performed three submaximal laboratory running tests on a treadmill: i) a submaximal multistage test with speed increases (test 1: speed), ii)
Results
Table 1 presents the TPW modulated by both mathematical models (TPW1 and TPW2) and PW estimated by the technologies for each environment and running condition.
Discussion
The current study aimed to analyze the effects of environment and running conditions on PW estimated by five commercial technologies through the comparison with two mathematical models (TPW1 and TPW2) based on laws of physics, in different environments and running conditions The main findings were that the Stryd and PolarV technologies showed the closest agreement with the TPW1 (Stryd: r ≥ 0.947, PolarV: r ≥ 0.931) and TPW2 (Stryd: r ≥ 0.933, PolarV: r ≥ 0.932) models. On the other hand, the
Acknowledgments
The authors wish to thank the participants for their invaluable contribution to the study.
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