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The Accumulating Data to Optimally Predict Obesity Treatment (ADOPT) Core Measures Project: Rationale and Approach

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 multi-factorial, but at present we lack a comprehensive evidence bas...

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Autores principales: MacLean, Paul S., Rothman, Alexander J., Nicastro, Holly L., Czajkowski, Susan M., Agurs-Collins, Tanya, Rice, Elise L., Courcoulas, Anita P., Ryan, Donna H., Bessesen, Daniel H., Loria, Catherine M.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: 2018
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5973529/
https://www.ncbi.nlm.nih.gov/pubmed/29575780
http://dx.doi.org/10.1002/oby.22154
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author MacLean, Paul S.
Rothman, Alexander J.
Nicastro, Holly L.
Czajkowski, Susan M.
Agurs-Collins, Tanya
Rice, Elise L.
Courcoulas, Anita P.
Ryan, Donna H.
Bessesen, Daniel H.
Loria, Catherine M.
author_facet MacLean, Paul S.
Rothman, Alexander J.
Nicastro, Holly L.
Czajkowski, Susan M.
Agurs-Collins, Tanya
Rice, Elise L.
Courcoulas, Anita P.
Ryan, Donna H.
Bessesen, Daniel H.
Loria, Catherine M.
author_sort MacLean, Paul S.
collection PubMed
description 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 multi-factorial, but 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 understanding of individual variability in response to adult obesity treatment. We provide 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 collectively can generate this evidence base and is a first step in an ongoing process that can be refined as the science advances.
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spelling pubmed-59735292019-04-01 The Accumulating Data to Optimally Predict Obesity Treatment (ADOPT) Core Measures Project: Rationale and Approach MacLean, Paul S. Rothman, Alexander J. Nicastro, Holly L. Czajkowski, Susan M. Agurs-Collins, Tanya Rice, Elise L. Courcoulas, Anita P. Ryan, Donna H. Bessesen, Daniel H. Loria, Catherine M. Obesity (Silver Spring) Article 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 multi-factorial, but 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 understanding of individual variability in response to adult obesity treatment. We provide 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 collectively can generate this evidence base and is a first step in an ongoing process that can be refined as the science advances. 2018-04 /pmc/articles/PMC5973529/ /pubmed/29575780 http://dx.doi.org/10.1002/oby.22154 Text en http://www.nature.com/authors/editorial_policies/license.html#terms Users may view, print, copy, and download text and data-mine the content in such documents, for the purposes of academic research, subject always to the full Conditions of use:http://www.nature.com/authors/editorial_policies/license.html#terms
spellingShingle Article
MacLean, Paul S.
Rothman, Alexander J.
Nicastro, Holly L.
Czajkowski, Susan M.
Agurs-Collins, Tanya
Rice, Elise L.
Courcoulas, Anita P.
Ryan, Donna H.
Bessesen, Daniel H.
Loria, Catherine M.
The Accumulating Data to Optimally Predict Obesity Treatment (ADOPT) Core Measures Project: Rationale and Approach
title The Accumulating Data to Optimally Predict Obesity Treatment (ADOPT) Core Measures Project: Rationale and Approach
title_full The Accumulating Data to Optimally Predict Obesity Treatment (ADOPT) Core Measures Project: Rationale and Approach
title_fullStr The Accumulating Data to Optimally Predict Obesity Treatment (ADOPT) Core Measures Project: Rationale and Approach
title_full_unstemmed The Accumulating Data to Optimally Predict Obesity Treatment (ADOPT) Core Measures Project: Rationale and Approach
title_short The Accumulating Data to Optimally Predict Obesity Treatment (ADOPT) Core Measures Project: Rationale and Approach
title_sort accumulating data to optimally predict obesity treatment (adopt) core measures project: rationale and approach
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5973529/
https://www.ncbi.nlm.nih.gov/pubmed/29575780
http://dx.doi.org/10.1002/oby.22154
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