The role of physical activity (PA) in many chronic diseases is increasingly being recognized. The accurate and detailed measurement of PA is a crucial prerequisite to further explore its association with health and disease. Small and wearable accelerometers allow objective measurement of PA (PA counts), while also providing a rough estimation of the energy expenditure associated with PA (EEAcT). However, current PA monitors are restricted to using time-averaged signals and linear regression algorithms which consistently provide inaccurate predictions of EEAc-r- the key characteristic of PA intensity. To fundamentally guide the future designs of PA monitors to accurately predict EEAcT, we hypothesize that parameters can be extracted from the raw acceleration and postural signals of multiple body segments. Using a unique combination of sophisticated instruments and technical expertise, we propose to develop a novel analytical approach for accurately predicting EEAc-reutilizing the raw acceleration signals from upper and lower body segments. This is accomplished by continuously measuring movement and postures, at a rate of 32 samples/second, using a custom-designed monitor that consists of an array of 10 accelerometers. We will measure minute-to minute EEAcT using a whole-room indirect calorimeter for a 24-hour period, and a portable calorimeter for a 3-hour free-living period. This measured EEAcT will be used as the target for the prediction model. We will apply an advanced modeling technique (artificial neural networks) to model the extracted PA parameters to arrive at an accurate prediction of EEAcT. Repeated measurements will be used to cross-validate the prediction accuracy of the model. The study is designed to encompass a heterogeneous population sample (n=200) of obese, overweight, and lean adults, and to include a wide range of PA types and intensities. The significance of this research is that our results will provide insight for developing the next-generation PA monitors, such as where on the body the sensors should be placed, what signal parameters should be extracted, and how the analytical algorithms should be applied. In addition, our study will improve and validate EEAcT prediction by several market-available PA monitors, thus offering immediate benefits to their applications in the field.

Agency
National Institute of Health (NIH)
Institute
National Institute of Diabetes and Digestive and Kidney Diseases (NIDDK)
Type
Research Project (R01)
Project #
5R01DK069465-02
Application #
6941312
Study Section
Special Emphasis Panel (ZRG1-HOP-D (50))
Program Officer
Miles, Carolyn
Project Start
2004-09-01
Project End
2009-07-31
Budget Start
2005-08-01
Budget End
2006-07-31
Support Year
2
Fiscal Year
2005
Total Cost
$335,099
Indirect Cost
Name
Vanderbilt University Medical Center
Department
Internal Medicine/Medicine
Type
Schools of Medicine
DUNS #
004413456
City
Nashville
State
TN
Country
United States
Zip Code
37212
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