Limitations of Foot-Worn Sensors for Assessing Running Power.

Tobias Baumgartner,Lars Donath,Stefanie Klatt,Steffen Held

doi:10.3390/s21154952

Tobias Baumgartner, Lars Donath + Show 2 more

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https://doi.org/10.3390/s21154952

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Abstract

Running power as measured by foot-worn sensors is considered to be associated with the metabolic cost of running. In this study, we show that running economy needs to be taken into account when deriving metabolic cost from accelerometer data. We administered an experiment in which 32 experienced participants (age = 28 ± 7 years, weekly running distance = 51 ± 24 km) ran at a constant speed with modified spatiotemporal gait characteristics (stride length, ground contact time, use of arms). We recorded both their metabolic costs of transportation, as well as running power, as measured by a Stryd sensor. Purposely varying the running style impacts the running economy and leads to significant differences in the metabolic cost of running (p < 0.01). At the same time, the expected rise in running power does not follow this change, and there is a significant difference in the relation between metabolic cost and power (p < 0.001). These results stand in contrast to the previously reported link between metabolic and mechanical running characteristics estimated by foot-worn sensors. This casts doubt on the feasibility of measuring running power in the field, as well as using it as a training signal.

Highlights

IntroductionFoot-worn sensors to assess and meaningfully analyze running metrics (e.g., step frequency, stride length, ground contact time) have gained increased attention and popularity [1,2,3,4,5]
In the last decade, foot-worn sensors to assess and meaningfully analyze running metrics have gained increased attention and popularity [1,2,3,4,5]
If more than 10% of the data points in a condition were within the 95% confidence interval (CI) of the baseline measurement, we considered this condition to not be a significantly different V O2

Summary

Introduction

Foot-worn sensors to assess and meaningfully analyze running metrics (e.g., step frequency, stride length, ground contact time) have gained increased attention and popularity [1,2,3,4,5]. These sensors are meant to improve laboratory and in-field testing and training by delivering key performance data. Two runners may cover a certain distance in the same duration, yet they experience different levels of exhaustion or underlying energy costs of running This is mainly due to a complex interplay among the central and peripheral properties of cardiocirculatory, ventilatory, metabolic, and psychological capacities [11]

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