Abstract: Measuring and Modeling Impulsivity Related to Eating Behaviors in Adolescents (Society for Prevention Research 21st Annual Meeting)

336 Measuring and Modeling Impulsivity Related to Eating Behaviors in Adolescents

Schedule:
Thursday, May 30, 2013
Pacific N/O (Hyatt Regency San Francisco)
* noted as presenting author
Matthew George Cox, PhD, Post Doctoral Student, Arizona State University, Tempe, AZ
David Peter MacKinnon, PhD, Professor, Arizona State University, Tempe, AZ
Sarah Boyle, MS, Student-Doctoral, Claremont Graduate University, Claremont, CA
Susan L. Ames, PhD, Associate Professor, Claremont Graduate University, Claremont, CA
Introduction: Impulsivity has been associated with a number of negative health behaviors including substance use (Acton, 2003), overeating (Braet, Claus, Verbeken,  & Van Vlierberghe, 2007) and obesity (Nederkoorna, Braetb, Van Eijsa, Tanghec,& Jansena, 2006). Several types of impulsivity exist as well as various measures of these different types.  As part of a study to examine the neuro-correlates of eating behaviors in adolescents, several measures of impulsivity were administered to 198 children ages 14 to 17 years old.  The measures included the Iowa Gambling Task (IGT), a generic Go/No-Go Task, a food specific Go/No-Go Task, and the Barrett Impulsivness Scale (BIS).  The IGT and the two Go/No-Go tasks require participants to perform a certain task over a certain number of trials. 

 Methods:  In order to create latent variables out of the IGT and the two Go/No-Go tasks parceling.  Item parceling (Bandaloos, 2002; Sterba, 2011) is the average or sum of unidimensional items to create a smaller number of indicators for a latent variable.  Reducing the number of items, in theory, increases model fit.  A measurement model was run, correlating the four measures of impulsivity to examine the overlap between the measures. 

Results: Results from the model showed adequate fit (RMSEA = .051, CFI = .950, TLI = .940), but the correlations between the measures suggest that most measures were not significantly related (p > .05).  The only significant correlation was between the generic Go/No-Go and the food Go/No-Go (r = .813, p < .001). 

Conclusions: Results from this data suggest that these measures are assessing different components of impulsivity which indicate that impulsivity is a multifaceted construct.  It may be that the constituent components of impulsivity deferentially impact eating behaviors and other relate outcome variables.  Understanding the influence of these different components is important to understanding and influencing outcome variables such as eating behaviors.