The energy expended in physical activity (EEAcr) is a major factor in energy balance. Since small energy excess (intake>expenditure) over time is the most common cause for weight gain and obesity in humans, the precision in measuring EEACT is crucial for etiological and interventional studies. Adolescents are at high risk for obesity, yet the intermittent nature of their physical activity (PA) and their greater metabolic efficiency make measuring EEAcr more challenging. The existing technologies in accelerometry are restricted to using crude time-averaged signals and over-simplified linear regression algorithms, which lead to inaccuracies in EEAcr predictions. Previous validation studies of these technologies often had imprecise measures of EE, lacked spontaneous and lower intensity PA's, and suffered from insufficient sample sizes. In our proposed study, we will test this hypothesis that measurements of detailed motion and postural signals from different body segments will significantly improve our predictions of EEACr as compared to the existing devices and algorithms used currently for adolescents.
In Specific Aim 1, we will obtain goldstandard EE measurements synchronously with PA measured using currently-available and custom designed accelerometry devices. We will measure minute-to-minute EEACr using a whole-room indirect calorimeter for a 24-hour period, and a portable calorimeter for a 3-hour free-living period. The new accelerometry device specifically developed for this study will synchronously measure accelerations at 32 samples/second, 16 channels, and 10 different body locations.
In Specific Aim 2, we propose to develop advanced modeling approaches to transform body accelerations and postures to predict EEACr- We will utilize powerful Artificial Neural Network and traditional biostatistical approaches for modeling.
In Specific Aim 3, we will validate these EE predictive models in free-living adolescents using wireless devices and doubly-labeled water. We will then, in Specific Aim 4, define the role of PA in energy balance for lean, overweight, and obese adolescents. In this study, we will apply the latest advances in bioengineering devices and analytical modeling in clinical investigations of adolescent obesity. This will also guide the design of future PA monitors targeting accurate energy expenditure assessments in this vulnerable population. Moreover, we will utilize our comprehensive measurements to validate and improve the ability of existing accelerometers to predict energy expenditure in adolescents, thus providing directly benefits to the field researchers.
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