title: Neural network versus activity-specific prediction equations for energy expenditure estimation in children.
abstract: The aim of this study was to compare the energy expenditure (EE) estimations of activity-specific prediction equations (ASPE) and of an artificial neural network (ANNEE) based on accelerometry with measured EE. Forty-three children (age: 9.8 ± 2.4 yr) performed eight different activities. They were equipped with one tri-axial accelerometer that collected data in 1-s epochs and a portable gas analyzer. The ASPE and the ANNEE were trained to estimate the EE by including accelerometry, age, gender, and weight of the participants. To provide the activity-specific information, a decision tree was trained to recognize the type of activity through accelerometer data. The ASPE were applied to the activity-type-specific data recognized by the tree (Tree-ASPE). The Tree-ASPE precisely estimated the EE of all activities except cycling [bias: -1.13 ± 1.33 metabolic equivalent (MET)] and walking (bias: 0.29 ± 0.64 MET; P < 0.05). The ANNEE overestimated the EE of stationary activities (bias: 0.31 ± 0.47 MET) and walking (bias: 0.61 ± 0.72 MET) and underestimated the EE of cycling (bias: -0.90 ± 1.18 MET; P < 0.05). Biases of EE in stationary activities (ANNEE: 0.31 ± 0.47 MET, Tree-ASPE: 0.08 ± 0.21 MET) and walking (ANNEE 0.61 ± 0.72 MET, Tree-ASPE: 0.29 ± 0.64 MET) were significantly smaller in the Tree-ASPE than in the ANNEE (P < 0.05). The Tree-ASPE was more precise in estimating the EE than the ANNEE. The use of activity-type-specific information for subsequent EE prediction equations might be a promising approach for future studies.
authors.affiliation: Swiss Federal Institute of Sport, Magglingen, Switzerland;
authors.forename: Nicole
authors.initials: N
authors.lastname: Ruch
authors.forename: Franziska
authors.initials: F
authors.lastname: Joss
authors.forename: Gerda
authors.initials: G
authors.lastname: Jimmy
authors.forename: Katarina
authors.initials: K
authors.lastname: Melzer
authors.forename: Johanna
authors.initials: J
authors.lastname: Hänggi
authors.forename: Urs
authors.initials: U
authors.lastname: Mäder
mesh_terms: D000054:Acceleration; D061725:Accelerometry / Q000379:methods; D002648:Child; D004734:Energy Metabolism / Q000502:physiology*; D005260:Female; D006801:Humans; D008297:Male; D055686:Metabolic Equivalent / Q000502:physiology*; D016571:Neural Networks, Computer; D016138:Walking / Q000502:physiology
keywords: accelerometer; automated pattern recognition; child; energy metabolism; physical activity