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.

doi:10.1002/oby.22154

Cited by 136 publications

(95 citation statements)

References 50 publications

(72 reference statements)

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“…Third, it is challenging to find generalizable data on individuals: most individual‐level data are collected from a unique set of sources (e.g., schools, employers) that often do not represent an unbiased sample of an entire community. There are several initiatives under way to improve measurement for obesity‐related research, such as the Accumulating Data to Optimally Predict Obesity Treatment (ADOPT) standards .…”

Section: Discussionmentioning

confidence: 99%

Methods and Risks of Bias in Natural Experiments in Obesity: Opportunities for the Future Informed by a Systematic Review

Knapp

Bennett

Wilson

et al. 2019

Obesity

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Objective This paper promotes rigorous methods and designs currently underutilized in obesity research, informed by a recent systematic review of the methods and risks of bias in studies of policies, programs, and built environment changes for obesity prevention and control. Methods To determine the current state of the field, relevant databases from 2000 to 2017 were searched to identify studies that fit the inclusion criteria. Study design, analytic approach, and other details of study methods were abstracted. These findings inform recommendations for obesity researchers and the field as a whole. Results Previously identified were 156 natural experiment studies. Most were cross‐sectional (35%), pre‐post single group comparison (31%), or difference‐in‐differences designs (29%). Few used rigorous causal designs such as interrupted time series with more than two time points, propensity score methods, or instrumental variables. The potential relevance for obesity research is discussed, and recommendations for obesity researchers are provided. Conclusions To strengthen natural experiment study designs and enhance the validity of results, researchers should carefully consider and control for confounding and selection of comparison groups and consider study designs that address these biases.

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Section: Discussionmentioning

confidence: 99%

Methods and Risks of Bias in Natural Experiments in Obesity: Opportunities for the Future Informed by a Systematic Review

Knapp

Bennett

Wilson

et al. 2019

Obesity

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“…In an implementation science framework, using the simplest and most practical measures customized to individual study and patient needs is a key. One promising development in obesity research is standardizing measurement across studies and developing core measures through a project known as Accumulating Data to Optimally Predict Obesity Treatment (ADOPT), which published six papers in Obesity in Spring, 2018 . By focusing on standardized activities and measures to prevent weight gain, patients with obesity may manage potential health harms individually, with a better understanding of maintaining lifestyle modifications long term.…”

Section: Issues and Potential Solutionsmentioning

confidence: 99%

Biobehavioural approaches to prevention and treatment: A call for implementation science in obesity research

Kaiser

Carson

Dhurandhar

et al. 2020

Obesity Science & Practice

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Summary Much progress has been made in the last 30 years in understanding the causes and mechanisms that contribute to obesity, yet widely available and successful strategies for prevention and treatment remain elusive at population levels. This paper discusses the biobehavioural framework and provides suggestions for applying it to enable greater progress in the science of obesity prevention and treatment, including an increased focus on implementation of science strategies. The objective is to promote a re‐evaluation of current views about preventing and treating obesity within a unified biobehavioural framework. Further integration of research exploring how both behavioural and biological components interact is a critical step forward.

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“…Results from this work raise the question of whether AN and obesity may be metabolic mirror images on some dimensions. Clinically, it is fairly easy for individuals with obesity to lose weight in the short‐term if they adhere to a ‘diet’, but weight regain is common, and interindividual variability in response to weight loss interventions is considerable . Explanations for weight regain are many and are controversial, but it may reflect a biologically driven process to settle at a previously attained high weight or fat mass .…”

Section: Geneticsmentioning

confidence: 99%

“…Clinically, it is fairly easy for individuals with obesity to lose weight in the short-term if they adhere to a 'diet', but weight regain is common, and interindividual variability in response to weight loss interventions is considerable. [53][54][55] Explanations for weight regain are many and are controversial, but it may reflect a biologically driven process to settle at a previously attained high weight or fat mass. 36 The field has not uniformly conceptualized AN as the opposite of obesity, with constitutional thinness (i.e., underweight, normal eating behavior, desire to gain weight, no cognitive features of AN) typically occupying that position, 56,57 and showing significant biological differences from AN.…”

Section: Genotype/ Gene Expression Environmentmentioning

confidence: 99%

Reconceptualizing anorexia nervosa

Bulik

Flatt

Abbaspour

et al. 2019

Psychiatry Clin Neurosci

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Anorexia nervosa (AN) has one of the highest mortality rates of any psychiatric disorder. Treatments are often ineffective and relapse is common. Most research attempting to understand the underlying causes and maintenance factors of AN has focused on environmental contributions, yet there is much to be explored in terms of biological risk and maintenance factors. In this paper, we focus primarily on AN research related to genetics and the complex microbial community in the gut (intestinal microbiota), and how these impact our conceptualization of this disorder. Emerging research identifying significant negative genetic correlations between AN and obesity suggests that the conditions may represent ‘metabolic bookends’. The identification of underlying biological mechanisms may provide both insight into extreme weight dysregulation on both ends of the spectrum and new possible points of entry for AN treatment. Additionally, the reported microbial imbalance (dysbiosis) in the gut microbiota in AN patients, potentially due to a nutrient‐ and energy‐deprived gut environment, implies alterations in functional and metabolic capacity of the gut microbiome. The extent to which AN and obesity can also be considered to be ‘microbiome bookends’ requires further investigation. Finally, we discuss ongoing and future AN projects exploring the interplay between host genomics, the environment, and cumulative microbial genomes (microbiome) as well as interventions at the microbial and gut level.

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

Cited by 136 publications

References 50 publications

Methods and Risks of Bias in Natural Experiments in Obesity: Opportunities for the Future Informed by a Systematic Review

Methods and Risks of Bias in Natural Experiments in Obesity: Opportunities for the Future Informed by a Systematic Review

Biobehavioural approaches to prevention and treatment: A call for implementation science in obesity research

Reconceptualizing anorexia nervosa

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