BACKGROUND/ OBJECTIVES: The purpose of this study was to develop an activity energy expenditure (AEE) prediction equation for the Actiheart activity monitor for use in children with chronic disease. SUBJECTS/ METHODS: In total, 63 children, aged 8-18 years with different types of chronic disease (juvenile arthritis, hemophilia, dermatomyositis, neuromuscular disease, cystic fibrosis or congenital heart disease) participated in an activity testing session, which consisted of a resting protocol, working on the computer, sweeping, hallway walking, steps and treadmill walking at three different speeds. During all activities, actual AEE was measured with indirect calorimetry and the participants wore an Actiheart on the chest. Resting EE and resting heart rate were measured during the resting protocol and heart rate above sleep (HRaS) was calculated. RESULTS: Mixed linear modeling produced the following prediction equation: This equation results in a nonsignificant mean difference of 2.1 J/kg/min (limits of agreement: -144.2 to 148.4 J/kg/min) for the prediction of AEE from the Actiheart compared with actual AEE. CONCLUSIONS: The Actiheart is valid for the use of AEE determination when using the new prediction equation for groups of children with chronic disease. However, the prediction error limits the use of the equation in individual subjects.
BACKGROUND/ OBJECTIVES: The purpose of this study was to develop an activity energy expenditure (AEE) prediction equation for the Actiheart activity monitor for use in children with chronic disease. SUBJECTS/ METHODS: In total, 63 children, aged 8-18 years with different types of chronic disease (juvenile arthritis, hemophilia, dermatomyositis, neuromuscular disease, cystic fibrosis or congenital heart disease) participated in an activity testing session, which consisted of a resting protocol, working on the computer, sweeping, hallway walking, steps and treadmill walking at three different speeds. During all activities, actual AEE was measured with indirect calorimetry and the participants wore an Actiheart on the chest. Resting EE and resting heart rate were measured during the resting protocol and heart rate above sleep (HRaS) was calculated. RESULTS: Mixed linear modeling produced the following prediction equation: This equation results in a nonsignificant mean difference of 2.1 J/kg/min (limits of agreement: -144.2 to 148.4 J/kg/min) for the prediction of AEE from the Actiheart compared with actual AEE. CONCLUSIONS: The Actiheart is valid for the use of AEE determination when using the new prediction equation for groups of children with chronic disease. However, the prediction error limits the use of the equation in individual subjects.
Authors: Cintia González; Pau Herrero; José M Cubero; José M Iniesta; M Elena Hernando; Gema García-Sáez; Alvaro J Serrano; Iñaki Martinez-Sarriegui; Carmen Perez-Gandia; Enrique J Gómez; Esther Rubinat; Valeria Alcantara; Eulalia Brugués; Ana Chico; Eugenia Mato; Olga Bell; Rosa Corcoy; Alberto de Leiva Journal: J Diabetes Sci Technol Date: 2013-07-01
Authors: Simone H Crouch; Lisa J Ware; Lebo F Gafane-Matemane; Herculina S Kruger; Tertia Van Zyl; Bianca Van der Westhuizen; Aletta E Schutte Journal: J Clin Hypertens (Greenwich) Date: 2018-07-01 Impact factor: 3.738
Authors: Mark A Ferro; Ellen L Lipman; Ryan J Van Lieshout; Brian Timmons; Lilly Shanahan; Jan Willem Gorter; Kathy Georgiades; Michael Boyle Journal: J Can Acad Child Adolesc Psychiatry Date: 2021-05-01
Authors: Mark A Ferro; Ellen L Lipman; Ryan J Van Lieshout; Jan Willem Gorter; Lilly Shanahan; Michael Boyle; Kathy Georgiades; Brian Timmons Journal: BMJ Open Date: 2019-11-03 Impact factor: 2.692