Abstract
Due to the nature of micro-electromechanical systems, the vector magnitude (VM) activity of accelerometers varies depending on the wearing position and does not identify different levels of physical fitness. Without an appropriate energy expenditure (EE) estimation equation, bias can occur in the estimated values. We aimed to amend the EE estimation equation using heart rate reserve (HRR) parameters as the correction factor, which could be applied to athletes and non-athletes who primarily use ankle-mounted devices. Indirect calorimetry was used as the criterion measure with an accelerometer (ankle-mounted) equipped with a heart rate monitor to synchronously measure the EE of 120 healthy adults on a treadmill in four groups. Compared with ankle-mounted accelerometer outputs, when the traditional equation was modified using linear regression by combining VM with body weight and/or HRR parameters (modified models: Model A, without HRR; Model B, with HRR), both Model A (r: 0.931 to 0.972; ICC: 0.913 to 0.954) and Model B (r: 0.933 to 0.975; ICC: 0.930 to 0.959) showed the valid and reliable predictive ability for the four groups. With respect to the simplest and most reasonable mode, Model A seems to be a good choice for predicting EE when using an ankle-mounted device.
Highlights
The difference between athletes’ and non-athletes’ resting metabolic rates (RMRs) and total energy expenditure (TEE) has been proven by researchers through measurements of golden standards[3,5,6]
Linear regression equations were determined for each physical fitness level based on average accelerometer vector magnitude (VM), body weight (BW), and without/with heart rate reserve (HRR) parameters (i.e., modifying the coefficients of traditional estimation equations (Model A) and Model B, respectively), and the corresponding EE measured by indirect calorimetry
The results indicated a significant difference in criterion measure (CM)-EE measurements among these four groups (p < 0.001)
Summary
The difference between athletes’ and non-athletes’ resting metabolic rates (RMRs) and total energy expenditure (TEE) has been proven by researchers through measurements of golden standards (indirect calorimetry or doubly labelled water)[3,5,6]. The manual of the research-level ActiGraph accelerometer (Actigraph Corporation, Pensacola, FL, USA) suggests that users wear the device on the wrist or waist Some products, such as the Nike+ sensor (Nike Inc, OR, USA) and the Adidas miCoach Footpod sensor (Adidas AG, Germany), are foot-mounted, and their EE values are measured mainly based on accelerometer signals and the distance recorded by a global positioning system (GPS). Our previous studies showed that including heart rate reserve (HRR) parameters in the prediction equation can further improve the reliability and validity of EE estimates, especially for individual physical fitness and in various wearing positions[30,31]. We presumed that the HRR parameter is an important indicator for calibrating the physical fitness of different groups, can be used to calibrate the bias of EE estimated by ankle-mounted devices, and can further enhance the reliability and validity of EE. We aimed to include HRR parameters in the EE prediction equation as a calibration indicator to adjust the EE prediction equation primarily for ankle-mounted devices among four groups, which included non-endurance athlete, endurance athlete, sedentary, and exercise-habit groups
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