Skip to main content
ARS Home » Research » Publications at this Location » Publication #216246
Title: Modeling growth curves to track growing obesity

Author
item GOSSETT, JEFF - ACHRI-DAC
item SIMPSON, PIPPA - ACHRI-DAC
item CASEY, PATRICK - ACHRI-DAC
item WHITESIDE-MANSELL, LEANNE - ACHRI-DAC
item BRADLEY, ROBERT - ACHRI-DAC
item JO, CHAN-HEE - ACHRI-DAC
item Bogle, Margaret

Submitted to: International Society for Behavioral Nutrition and Physical Activity
Publication Type: Abstract Only
Publication Acceptance Date: 4/20/2007
Publication Date: 6/16/2007
Citation: Gossett, J., Simpson, P., Casey, P., Whiteside-Mansell, L., Bradley, R., Jo, C., Bogle, M.L. 2007. Modeling growth curves to track growing obesity [abstract]. Proceedings of the International Society for Behavioral Nutrition and Physical Activity. p. 195.

Interpretive Summary:

Technical Abstract: Our purpose was to examine the relationship between total physical activity (PA) and PA at various intensity levels with insulin resistance at increasing waist circumference and skinfold thickness levels. Being able to describe growth appropriately and succinctly is important in many nutrition and physical exercise interventions. Various approaches are used varying from differential equations, deterministic modeling, and statistical approaches like regression. Often with epidemiologic data we want to model growth in the context of demographic variables and other potential mediators. However, growth is non-linear, so the addition of covariates may be problematic. Additionally, measurements may be unevenly spaced and there may even be missing data so some form of modeling that will deal with this is needed. We investigate potential growth models using anthropometric data like height, weight, and body mass index, from the infant Health and Development Program (HDP). This study has measurements for at least 8 time points. We investigate how missing data and covariate can be considered and characterize their growth by gender subgroups in a similar way to the CRC growth curves. The difficulties posed by non -inear modeling can be dealt with using the appropriate models. This will enable better longitudinal tracking of the effects of interventions on obesity.