The current epidemic of obesity in the United States and much of the rest of the world may be related to the decreased physical activity (PA) characteristic of modem life. Definition of the specific relationship of PA and obesity has been hampered by inadequate PA assessment methodology. We have recently validated a newly available PA measurement device (IDEEA) and found it to detect type, duration and intensity of many types of PA at better than 98% accuracy. We propose to use this device to describe specific PA of adolescent girls. But prior to beginning that study, we will further evaluate IDEEA to determine if it can also be used to accurately assess energy expenditure (EE). We will do this by validating it against the gold standards for EE measurement - respirometry indirect calorimetry and doubly labeled water. Specifically, we will compare IDEEA-derived estimates for EE, with: 1) EE measured for specific PA by hood respirometry, 2) EE measured by 24-hour chamber respirometry, and 3) free-living EE assessed by doubly labeled water. We will then use IDEEA to collect cross-sectional data on free-living PA and EE in adolescent girls. We choose this group because adolescence is a critical period for development of obesity, particularly in females. Adolescent obesity tracks not only with adult obesity, but also with other risk factors independent of adult weight. This is also the period of racial divergence in adiposity. We will therefore use this device to assess type, duration and intensity of PA and EE in free-living Caucasian and African-American girls, ages 11-16. Body composition (fat mass and fat-free mass) will then be modeled as a function of age, ethnicity, sexual maturation (Tanner stage) and specific free-living physical activities and associated EE. Findings from these studies will provide critical validation of this important new PA measurement device to the research community. They will also provide a far more complete description of PA in adolescent girls than has ever been possible before. Finally, data from these studies will provide the basis for a future application to conduct a longitudinal, PA intervention study in this population.
| 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 |
| Zhang, Kuan; Pi-Sunyer, F Xavier; Boozer, Carol N (2004) Improving energy expenditure estimation for physical activity. Med Sci Sports Exerc 36:883-9 |
