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

P-Y de Müllenheim; S Chaudru; M Emily; M Gernigon; G Mahé; S Bickert; J Prioux; B Noury-Desvaux; A Le Faucheur

doi:10.1016/j.jsams.2017.10.004

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

J Sci Med Sport. 2018 Feb;21(2):166-172. doi: 10.1016/j.jsams.2017.10.004. Epub 2017 Oct 13.

Authors

P-Y de Müllenheim¹, S Chaudru², M Emily³, M Gernigon⁴, G Mahé⁵, S Bickert⁶, J Prioux⁷, B Noury-Desvaux⁸, A Le Faucheur⁹

Affiliations

¹ Movement, Sport and Health Laboratory (EA 1274), Faculty of Sport Sciences, Université de Rennes 2, France.
² Vascular Medicine Unit, University Hospital of Rennes, France.
³ IRMAR, CNRS UMR 6625, Agrocampus Ouest, France.
⁴ Laboratory for Vascular Investigations and Sports Medicine, University Hospital of Angers, France; Laboratory of Physiology, INSERM UMR 1083, CNRS UMR 6214, Medical School, University of Angers, France.
⁵ Vascular Medicine Unit, University Hospital of Rennes, France; Univ Rennes, CHU Rennes, INSERM, CIC 1414, F-35000 Rennes, France.
⁶ Laboratory for Vascular Investigations and Sports Medicine, University Hospital of Angers, France.
⁷ Movement, Sport and Health Laboratory (EA 1274), Faculty of Sport Sciences, Université de Rennes 2, France; Department of Sport sciences and physical education, Ecole normale supérieure de Rennes, France.
⁸ Laboratory for Vascular Investigations and Sports Medicine, University Hospital of Angers, France; Laboratory of Physiology, INSERM UMR 1083, CNRS UMR 6214, Medical School, University of Angers, France; Institute of Physical Education and Sports Sciences (IFEPSA), Université Catholique de l'Ouest (UCO), France.
⁹ Movement, Sport and Health Laboratory (EA 1274), Faculty of Sport Sciences, Université de Rennes 2, France; Univ Rennes, CHU Rennes, INSERM, CIC 1414, F-35000 Rennes, France; Department of Sport sciences and physical education, Ecole normale supérieure de Rennes, France. Electronic address: alexis.lefaucheur@ens-rennes.fr.

PMID: 29110991
DOI: 10.1016/j.jsams.2017.10.004

Abstract

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 (K4b²). 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.

Keywords: Accelerometer; Energy metabolism; Exercise; Global positioning system; Methods; Public health.

Publication types

Comparative Study

MeSH terms

Accelerometry / methods*
Adult
Cross-Sectional Studies
Energy Metabolism / physiology*
Female
Geographic Information Systems*
Heart Rate / physiology*
Humans
Linear Models
Male
Monitoring, Physiologic / methods
Walking / physiology*
Wearable Electronic Devices
Young Adult