Using GPS, accelerometry and heart rate to predict outdoor graded walking energy expenditure.

Objectives: To determine the best method and combination of methods among global positioning system (GPS), accelerometry, and heart rate (HR) for estimating energy expenditure (EE) during level and graded outdoor walking.
Design: Thirty adults completed 6-min outdoor walks at speeds of 2.0, 3.5, and 5.0kmh ^-1 during three randomized outdoor walking sessions: one level walking session and two graded (uphill and downhill) walking sessions on a 3.4% and a 10.4% grade. EE was measured using a portable metabolic system (K4b ² ). Participants wore a GlobalSat ^® DG100 GPS receiver, an ActiGraph™ wGT3X+ accelerometer, and a Polar ^® HR monitor. Linear mixed models (LMMs) were tested for EE predictions based on GPS speed and grade, accelerometer counts or HR-related parameters (alone and combined). Root-mean-square error (RMSE) was used to determine the accuracy of the models. Published speed/grade-, count-, and HR-based equations were also cross-validated.
Results: According to the LMMs, GPS was as accurate as accelerometry (RMSE=0.89-0.90kcalmin ^-1 ) and more accurate than HR (RMSE=1.20kcalmin ^-1 ) for estimating EE during level walking; GPS was the most accurate method for estimating EE during both level and uphill (RMSE=1.34kcalmin ^-1 )/downhill (RMSE=0.84kcalmin ^-1 ) walking; combining methods did not increase the accuracy reached using GPS (or accelerometry for level walking). The cross-validation results were in accordance with the LMMs, except for downhill walking.
Conclusions: Our study provides useful information regarding the best method(s) for estimating EE with appropriate equations during level and graded outdoor walking.
(Copyright © 2017 Sports Medicine Australia. Published by Elsevier Ltd. All rights reserved.)