title: A comparison of energy expenditure estimation of several physical activity monitors.
abstract: BACKGROUND
  Accurately and precisely estimating free-living energy expenditure (EE) is important for monitoring energy balance and quantifying physical activity. Recently, single and multisensor devices have been developed that can classify physical activities, potentially resulting in improved estimates of EE.
  
  
  OBJECTIVE
  This study aimed to determine the validity of EE estimation of a footwear-based physical activity monitor and to compare this validity against a variety of research and consumer physical activity monitors.
  
  
  METHODS
  Nineteen healthy young adults (10 men, 9 women) completed a 4-h stay in a room calorimeter. Participants wore a footwear-based physical activity monitor as well as Actical, ActiGraph, IDEEA, DirectLife, and Fitbit devices. Each individual performed a series of postures/activities. We developed models to estimate EE from the footwear-based device, and we used the manufacturer's software to estimate EE for all other devices.
  
  
  RESULTS
  Estimated EE using the shoe-based device was not significantly different than measured EE (mean ± SE; 476 ± 20 vs 478 ± 18 kcal, respectively) and had a root-mean-square error of 29.6 kcal (6.2%). The IDEEA and the DirectLlife estimates of EE were not significantly different than the measured EE, but the ActiGraph and the Fitbit devices significantly underestimated EE. Root-mean-square errors were 93.5 (19%), 62.1 kcal (14%), 88.2 kcal (18%), 136.6 kcal (27%), 130.1 kcal (26%), and 143.2 kcal (28%) for Actical, DirectLife, IDEEA, ActiGraph, and Fitbit, respectively.
  
  
  CONCLUSIONS
  The shoe-based physical activity monitor provides a valid estimate of EE, whereas the other physical activity monitors tested have a wide range of validity when estimating EE. Our results also demonstrate that estimating EE based on classification of physical activities can be more accurate and precise than estimating EE based on total physical activity.
authors.affiliation: 1Health and Exercise Science, Colorado State University, Fort Collins, CO; 2Electrical and Computer Engineering, University of Alabama, Tuscaloosa, AL; 3Division of Endocrinology, Metabolism and Diabetes, University of Colorado Anschutz Medical Campus, Denver, CO; and 4Division of Geriatrics, University of Colorado Anschutz Medical Campus, Denver, CO.
authors.forename: Kathryn L
authors.initials: KL
authors.lastname: Dannecker
authors.forename: Nadezhda A
authors.initials: NA
authors.lastname: Sazonova
authors.forename: Edward L
authors.initials: EL
authors.lastname: Melanson
authors.forename: Edward S
authors.initials: ES
authors.lastname: Sazonov
authors.forename: Raymond C
authors.initials: RC
authors.lastname: Browning
mesh_terms: D061725:Accelerometry / Q000295:instrumentation*; D001642:Bicycling / Q000502:physiology; D004734:Energy Metabolism; D005260:Female; D006801:Humans; D008297:Male; D008991:Monitoring, Physiologic / Q000295:instrumentation*; D009043:Motor Activity / Q000502:physiology; D010101:Oxygen Consumption; D011187:Posture / Q000502:physiology; D012775:Shoes; D016138:Walking / Q000502:physiology