Through this application, the candidate for this Research Career Award (K25) seeks additional training in conducting human research, with a special emphasis on applying engineering techniques to improve the estimation of energy cost of spontaneous physical activities (PA). His immediate goal is to develop additional expertise in biomedical research and clinical investigation; his ultimate goal is to develop an independent research program in assessment of PA and energy expenditure (EE) as it relates to obesity and the metabolic syndrome. Spontaneous PA is a significant part of total EE. Energy requirements of usual activities, such as sitting, standing and lying down have been studied extensively. However, the energy costs for many spontaneous activities, such as fidgeting and transitions between activities, which are known to substantively contribute to energy balance, have not been well studied and quantified. It is important to develop effective methods to assess those activities in order to complete accurate 24-hour EE assessment.
The specific aims of this application are: 1) To quantify spontaneous PA and improve energy cost estimation of PA in free-living individuals by developing mathematical models of each of 40 spontaneous activities for mechanical work and energy cost calculation. 2) To calibrate and validate the above models and methods with subjects performing various activities using indirect calorimetry and a large force platform. These studies will improve the overall accuracy for energy cost estimation of the existing technology and provide the techniques to quantify the impact of certain spontaneous activity on energy expenditure. It also will provide a convenient and practical method to assess daily PA effectively and accurately. The ability to obtain quantitative information of daily PA will assist specialists and physicians in developing and evaluating strategies to promote PA for improving fitness and treating obesity as well as other diseases.
| Zhang, Kuan; Sun, Ming; Lester, D Kevin et al. (2005) Assessment of human locomotion by using an insole measurement system and artificial neural networks. J Biomech 38:2276-87 |
