No abstract

Background: Physical activity and corresponding energy expenditure can improve health in various ways. Existing methods to directly measure energy expenditure are currently limited to laboratory settings and/or expensive instrumentation. The purpose of this study was to evaluate accuracy of energy expenditure characterization, during walking and running, using demographic data, as well as data collected via an accelerometer and novel piezoresponsive foam sensors. Methods: 30 individuals (14 females; mass = 67 ± 10 kg; height = 1.74 ± 0.08 m; age = 23 ± 3 yrs) walked and ran at five speeds (1.34, 2.23, 2.68, 3.13, and 3.58 m/s) on a force-instrumented treadmill while wearing a metabolic analyzer and standardized athletic shoes instrumented with an accelerometer, and four novel nanocomposite piezoresponsive force sensors. Various predictive models, including demographic data and data derived from the accelerometer and force sensors, were evaluated for each gait speed. Results: The predictive models varied in ability to accurately characterize energy expenditure. For walking, the most accurate model included acceleration and body weight, and resulted in an average absolute error of 0.07 ± 0.03 kcal/min. For running, the most accurate model included sensor and acceleration data, and resulted in an average absolute error of 0.45 ± 0.14 kcal/min. Conclusions: When combined with acceleration data and body weight, the novel foam sensors can be used to inexpensively and accurately measure walking and running energy expenditure. This can be done at various speeds, outside of a traditional research laboratory. These results have application within a wide range of diverse contexts.