Journal of Adolescent Health
Volume 45, Issue 5 , Pages 517-524, November 2009

One Size Does Not Fit All: Identifying Risk Profiles for Overweight in Adolescent Population Subsets

  • Rhonda BeLue, M.S., Ph.D.

      Affiliations

    • Department of Health Policy and Administration, The Pennsylvania State University, University Park, Pennsylvania
    • Corresponding Author InformationAddress correspondence to: Rhonda BeLue, M.S., Ph.D., Department of Health Policy and Administration, The Pennsylvania State University, University Park, PA 16802.
  • ,
  • Lori Ann Francis, Ph.D.

      Affiliations

    • Department of Biobehavioral Health, The Pennsylvania State University, University Park, Pennsylvania
  • ,
  • Brandi Rollins, M.S.

      Affiliations

    • Department of Human Development and Family Studies, The Pennsylvania State University, University Park, Pennsylvania
  • ,
  • Brendon Colaco, M.D., M.H.A.

      Affiliations

    • Department of Health Policy and Administration, The Pennsylvania State University, University Park, Pennsylvania

Received 2 July 2008; accepted 17 March 2009. published online 28 May 2009.

Abstract 

Purpose

The purpose of this study is to identify population subgroups of adolescents who are homogenous with respect to sociodemographic factors and potentially modifiable risk and protective factors related to overweight status in a nationally representative sample of adolescents ages 12–17.

Methods

The data used for this study are from the Centers for Disease Control and National Center for Health Statistics' National Survey of Children's Health, 2003 (NSCH). Classification and Regression Trees (CART) were used to identify population segments of adolescents based on risk and protective factors for obesity.

Results

In the final CART model, 12 variables remained, including: poverty level, race, gender, participation in sports, number of family meals, family educational attainment, child physical activity, participation in free lunch programs, neighborhood safety and connectedness, TV viewing time, and child age in years. Poverty level was determined to be the most variable related to weight status in this sample of adolescents. Adolescents living in households below approximately the 300% poverty level were subject to a different constellation of predictors than adolescents living in homes above the 300% poverty level.

Conclusions

Our results demonstrate how risk and protective factors related to obesity emerge differently among sociodemographic subgroups and the relative importance of these risk and protective factors in relation to adolescent overweight status. Interventions that work for one population subgroup may not work for another.

Keyword: Adolescent obesity

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PII: S1054-139X(09)00120-7

doi:10.1016/j.jadohealth.2009.03.010

Journal of Adolescent Health
Volume 45, Issue 5 , Pages 517-524, November 2009