Validation of the Actiheart activity monitor for measurement of activity energy expenditure in children and adolescents with chronic disease

Abstract

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:Estimated AEE ðJ=kg= minÞ ¼ À93:7 þ ð4:8ÂHRaSÞþð0:04ÂAccelerometer CountsÞþð39:8ÂGender ðgirls ¼ 0; boys ¼ 1ÞÞ:This equation results in a nonsignificant mean difference of 2.1 J/kg/min (limits of agreement: À144.2 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.

Validation of Accelerometer Prediction Equations in Children with Chronic Disease

“…However -it is unlikely that any prediction equation derived from healthy children would provide good accuracy; we have found a significant interaction between accelerometer counts and diagnosis that suggests that the relationship between accelerometer counts and EE is different in children with chronic conditions as compared to healthy controls and when compared to other disease groups (43). As an example, in our previous study we tested the validity of the Actiheart accelerometer in the same population and found that a newly developed prediction equation tended to over-estimate energy expenditure in our chronic disease group (43).…”

“…Differences in EE and accelerometer counts could be attributed to factors related to disease pathophysiology, sequelae, and drug treatment (42,43). For example, mechanical efficiency due to joint damage or contractures or muscle atrophy from medication and muscle disuse in JA or JDM (23,42); decreased lung function in CF (36) and disturbed muscle metabolism in IMD as well as deconditioning or poor neuromotor control may explain the differences in patterns of movement and increased energy cost of performing physical activities (8,24).…”

Exercise as Medicine for Children with Arthritis

“…While many measures of fitness are very stable (reliable), we and others have found that activity questionnaires for children are much less reliable 28 . Accelerometry, an objective measure of activity, has validity issues when applied to children with chronic diseases such as arthritis; this is probably because movement patterns and metabolic expenditure are affected in a variety of ways by illness 29,30 .…”

Energy expenditure: how much does it matter in infant and pediatric chronic disorders?

“…With the advent of technology, secondary objective methods such as portable heart rate monitors, pedometers, and accelerometers are now increasingly available and have been incorporated into the estimation of TEE in chronically ill patients (18). A combination of accelerometer and heart rate has been shown to provide accurate prediction of the physical activity energy expenditure (PAEE) in children (19,20). Accelerometers use piezoelectric transducers and microprocessors that convert acceleration produced by body movements into a digital signal.…”

“…Accelerometers use piezoelectric transducers and microprocessors that convert acceleration produced by body movements into a digital signal. In a recent study by Takken et al (19), a chest mounted, combined-unit was employed in a Dutch cohort of children aged 8 to 18 y, for synchronized heart rate and accelerometer recordings. These variables were then used to derive PAEE using a previously validated formula (20).…”