Weight loss outcomes achieved through conventional behavior change interventions are prone to deterioration over time. Basic learning laboratory studies in the area of behavioral extinction and renewal and multilevel models of weight control offer clues as to why newly acquired weight loss skills are prone to relapse. According to these models, current clinic-based interventions may not be of sufficient duration or scope to allow for the practice of new skills across the multiple community contexts necessary to promote sustainable weight loss. Although longer, more intensive interventions with greater reach may hold the key to improving weight loss outcomes, it is difficult to test these assumptions in a time efficient and cost-effective manner. A research design tool that has been increasingly utilized in other fields (e.g., pharmaceuticals) is the use of biosimulation analyses. The present study describes our research team's use of computer simulation models to assist in designing a study to test a novel, comprehensive socio-environmental treatment approach to weight loss maintenance in children ages 7-12 years. Weight outcome data from the weight loss, weight maintenance, and follow-up phases of a recently completed randomized controlled trial (RCT) were used to describe the time course of a proposed, extended multilevel treatment program. Simulations were then conducted to project the expected changes in child percent overweight (POW) trajectories in the proposed study. A 12.9% decrease in POW at 30 months was estimated based upon the midway point between models of "best-case" and "worst-case" weight maintenance scenarios. Preliminary data and further analyses, including biosimulation projections, suggest that our socio-environmental approach to weight loss maintenance treatment is promising and warrants evaluation in a large-scale RCT. Biosimulation techniques may have utility in the design of future community-level interventions for the treatment and prevention of childhood overweight.

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http://www.ncbi.nlm.nih.gov/pmc/articles/PMC3044501PMC
http://dx.doi.org/10.1038/oby.2009.437DOI Listing

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