Background: Individual variability in response to multiple modalities of obesity treatment is well documented, yet our understanding of why some individuals respond while others do not is limited. The etiology of this variability is multifactorial; however, at present, we lack a comprehensive evidence base to identify which factors or combination of factors influence treatment response. Objectives: This paper provides an overview and rationale of the Accumulating Data to Optimally Predict obesity Treatment (ADOPT) Core Measures Project, which aims to advance the understanding of individual variability in response to adult obesity treatment. This project provides an integrated model for how factors in the behavioral, biological, environmental, and psychosocial domains may influence obesity treatment responses and identify a core set of measures to be used consistently across adult weight-loss trials. This paper provides the foundation for four companion papers that describe the core measures in detail. Significance: The accumulation of data on factors across the four ADOPT domains can inform the design and delivery of effective, tailored obesity treatments. ADOPT provides a framework for how obesity researchers can collectively generate this evidence base and is a first step in an ongoing process that can be refined as the science advances.
Bibliographical noteFunding Information:
1 University of Colorado School of Medicine, Aurora, Colorado, USA. Correspondence: Paul S. MacLean (firstname.lastname@example.org) 2 Department of Psychology, University of Minnesota, Minneapolis, Minnesota, USA 3 National Heart, Lung, and Blood Institute, National Institutes of Health, Bethesda, Maryland, USA 4 National Cancer Institute, National Institutes of Health, Rockville, Maryland, USA 5 University of Pittsburgh Medical Center, Pittsburgh, Pennsylvania, USA 6 Pennington Biomedical Research Center, Louisiana State University, Baton Rouge, Louisiana, USA.
Funding agencies: The ADOPT Core Measures Working Group was supported by intramural funding from the following National Institutes of Health: The National Heart, Lung, and Blood Institute, the National Cancer Institute, and the Office of Disease Prevention. The Grid-Enabled Measures Database (GEM) is supported and administered by the National Cancer Institute. Disclosure: PSM and APC report grants and/or pending grant applications from NIH and PCORI that are directly relevant to this work. All other authors declared no conflicts of interest that are directly relevant to the work under consideration. The views expressed in this paper are those of the authors and do not necessarily represent the positions of the NIH, the DHHS, or the Federal Government. Additional Supporting Information may be found in the online version of this article. Received: 15 December 2017; Accepted: 12 February 2018; Published online 23 March 2018. doi:10.1002/oby.22154
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