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Accumulating Data to Optimally Predict Obesity Treatment (ADOPT) Core Measures: Psychosocial Domain
Author(s) -
Sutin Angelina R.,
Boutelle Kerri,
Czajkowski Susan M.,
Epel Elissa S.,
Green Paige A.,
Hunter Christine M.,
Rice Elise L.,
Williams David M.,
YoungHyman Deborah,
Rothman Alexander J.
Publication year - 2018
Publication title -
obesity
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.438
H-Index - 199
eISSN - 1930-739X
pISSN - 1930-7381
DOI - 10.1002/oby.22160
Subject(s) - psychosocial , weight loss , domain (mathematical analysis) , psychological intervention , clinical psychology , inclusion (mineral) , psychology , personality , affect (linguistics) , obesity , cognition , medicine , psychotherapist , psychiatry , social psychology , mathematics , mathematical analysis , communication
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 This paper describes (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.