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Accumulating Data to Optimally Predict obesity Treatment (ADOPT) Core Measures: Psychosocial Domain
BACKGROUND: Within the Accumulating Data to Optimally Predict obesity Treatment (ADOPT) Core Measures project, the psychosocial domain addresses how psychosocial processes underlie the influence of obesity treatment strategies on weight loss and weight maintenance. The subgroup for the psychosocial...
Autores principales: | , , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
2018
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7055940/ https://www.ncbi.nlm.nih.gov/pubmed/29575781 http://dx.doi.org/10.1002/oby.22160 |
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author | Sutin, Angelina R. Boutelle, Kerri Czajkowski, Susan M. Epel, Elissa S. Green, Paige A. Hunter, Christine M. Rice, Elise L. Williams, David M. Young-Hyman, Deborah Rothman, Alexander J. |
author_facet | Sutin, Angelina R. Boutelle, Kerri Czajkowski, Susan M. Epel, Elissa S. Green, Paige A. Hunter, Christine M. Rice, Elise L. Williams, David M. Young-Hyman, Deborah Rothman, Alexander J. |
author_sort | Sutin, Angelina R. |
collection | PubMed |
description | BACKGROUND: Within the Accumulating Data to Optimally Predict obesity Treatment (ADOPT) Core Measures project, the psychosocial domain addresses how psychosocial processes underlie the influence of obesity treatment strategies on weight loss and weight maintenance. The subgroup for the psychosocial domain identified an initial list of high-priority constructs and measures that ranged from relatively stable characteristics about the person (cognitive function, personality) to dynamic characteristics that may change over time (motivation, affect). OBJECTIVES: In this paper, we describe (a) how the psychosocial domain fits into the broader model of weight loss and weight maintenance as conceptualized by ADOPT; (b) the guiding principles used to select constructs and measures for recommendation; (c) the high priority constructs recommended for inclusion; (d) domain-specific issues for advancing the science; and (e) recommendations for future research. SIGNIFICANCE: The inclusion of similar measures across trials will help to better identify how psychosocial factors mediate and moderate the weight loss and weight maintenance process, facilitate research into dynamic interactions with factors in the other ADOPT domains, and ultimately improve the design and delivery of effective interventions. |
format | Online Article Text |
id | pubmed-7055940 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2018 |
record_format | MEDLINE/PubMed |
spelling | pubmed-70559402020-03-04 Accumulating Data to Optimally Predict obesity Treatment (ADOPT) Core Measures: Psychosocial Domain Sutin, Angelina R. Boutelle, Kerri Czajkowski, Susan M. Epel, Elissa S. Green, Paige A. Hunter, Christine M. Rice, Elise L. Williams, David M. Young-Hyman, Deborah Rothman, Alexander J. Obesity (Silver Spring) Article BACKGROUND: Within the Accumulating Data to Optimally Predict obesity Treatment (ADOPT) Core Measures project, the psychosocial domain addresses how psychosocial processes underlie the influence of obesity treatment strategies on weight loss and weight maintenance. The subgroup for the psychosocial domain identified an initial list of high-priority constructs and measures that ranged from relatively stable characteristics about the person (cognitive function, personality) to dynamic characteristics that may change over time (motivation, affect). OBJECTIVES: In this paper, we describe (a) how the psychosocial domain fits into the broader model of weight loss and weight maintenance as conceptualized by ADOPT; (b) the guiding principles used to select constructs and measures for recommendation; (c) the high priority constructs recommended for inclusion; (d) domain-specific issues for advancing the science; and (e) recommendations for future research. SIGNIFICANCE: The inclusion of similar measures across trials will help to better identify how psychosocial factors mediate and moderate the weight loss and weight maintenance process, facilitate research into dynamic interactions with factors in the other ADOPT domains, and ultimately improve the design and delivery of effective interventions. 2018-04 /pmc/articles/PMC7055940/ /pubmed/29575781 http://dx.doi.org/10.1002/oby.22160 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 Sutin, Angelina R. Boutelle, Kerri Czajkowski, Susan M. Epel, Elissa S. Green, Paige A. Hunter, Christine M. Rice, Elise L. Williams, David M. Young-Hyman, Deborah Rothman, Alexander J. Accumulating Data to Optimally Predict obesity Treatment (ADOPT) Core Measures: Psychosocial Domain |
title | Accumulating Data to Optimally Predict obesity Treatment (ADOPT) Core Measures: Psychosocial Domain |
title_full | Accumulating Data to Optimally Predict obesity Treatment (ADOPT) Core Measures: Psychosocial Domain |
title_fullStr | Accumulating Data to Optimally Predict obesity Treatment (ADOPT) Core Measures: Psychosocial Domain |
title_full_unstemmed | Accumulating Data to Optimally Predict obesity Treatment (ADOPT) Core Measures: Psychosocial Domain |
title_short | Accumulating Data to Optimally Predict obesity Treatment (ADOPT) Core Measures: Psychosocial Domain |
title_sort | accumulating data to optimally predict obesity treatment (adopt) core measures: psychosocial domain |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7055940/ https://www.ncbi.nlm.nih.gov/pubmed/29575781 http://dx.doi.org/10.1002/oby.22160 |
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