Childhood Obesity Prevention and Treatment Research (COPTR): Interventions addressing multiple influences in childhood and adolescent obesity

Pratt, Charlotte; Boyington, Josephine; Esposito, Layla; Pemberton, Victoria L.; Bonds, Denise E.; Kelley, Melinda S.; Yang, Song; Murray, David M.; Stevens, June

doi:10.1016/j.cct.2013.08.010

Cited by 44 publications

(45 citation statements)

References 40 publications

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“…5,9 Explanatory variables in the gender-specific equations were limited to those collected by the COPTR investigators, which included demographics plus four anthropometric measurements (height, weight, waist circumference and triceps skinfold). Forward and backward selection was used to develop equations in 2/3 of the sample, and the remaining 1/3 of the sample provided internal validation.…”

Section: Introductionmentioning

confidence: 99%

Prediction of percent body fat measurements in Americans 8 years and older

Stevens

Cai

et al. 2015

Int J Obes

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BACKGROUND/OBJECTIVES Although numerous equations to predict percent body fat have been published, few have broad generalizability. The objective of this study was to develop sets of equations that are generalizable to the American population 8 years of age and older. SUBJECTS/METHODS Dual-emission X-ray absorptiometry (DXA) assessed percent body fat from the 1999–2006 National Health and Nutrition Examination Survey (NHANES) was used as the response variable for development of 14 equations for each gender that included between 2 and 10 anthropometrics. Other candidate variables included demographics and menses. Models were developed using the Least Absolute Shrinkage and Selection Operator (LAASO) and validated in a ¼ withheld sample randomly selected from 11 884 males or 9215 females. RESULTS In the final models, R2 ranged from 0.664 to 0.845 in males and from 0.748 to 0.809 in females. R2 was not notably improved by development of equations within, rather than across, age and ethnic groups. Systematic over or under estimation of percent body fat by age and ethnic groups was within 1 percentage point. Seven of the fourteen gender-specific models had R2 values above 0.80 in males and 0.795 in females and exhibited low bias by age, race/ethnicity and body mass index (BMI). CONCLUSIONS To our knowledge, these are the first equations that have been shown to be valid and unbiased in both youth and adults in estimating DXA assessed body fat. The equations developed here are appropriate for use in multiple ethnic groups, are generalizable to the US population and provide a useful method for assessment of percent body fat in settings where methods such as DXA are not feasible.

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Section: Introductionmentioning

confidence: 99%

Prediction of percent body fat measurements in Americans 8 years and older

Stevens

Cai

et al. 2015

Int J Obes

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“…The consortia include the COPTR (four studies) [4], the Early Adult Reduction of weight through LifestYle intervention (EARLY; seven studies) [5], the Obesity-Related Behavioral Intervention Trials (ORBIT; seven studies) [6], and the Lifestyle Interventions For Expectant Moms (LIFE-Moms, seven studies). COPTR is testing multilevel intervention approaches to prevent excess weight gain in youth and to reduce weight among overweight and obese youth.…”

Section: Methodological Issues In Combining Similar But Different Intmentioning

confidence: 99%

Statistical methodologies to pool across multiple intervention studies

Bangdiwala

Bhargava

O’Connor

et al. 2016

Behav. Med. Pract. Policy Res.

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Combining and analyzing data from heterogeneous randomized controlled trials of complex multiplecomponent intervention studies, or discussing them in a systematic review, is not straightforward. The present article describes certain issues to be considered when combining data across studies, based on discussions in an NIH-sponsored workshop on pooling issues across studies in consortia (see Belle et al. in Psychol Aging, 18(3):396-405, 2003). Several statistical methodologies are described and their advantages and limitations are explored. Whether weighting the different studies data differently, or via employing random effects, one must recognize that different pooling methodologies may yield different results. Pooling can be used for comprehensive exploratory analyses of data from RCTs and should not be viewed as replacing the standard analysis plan for each study. Pooling may help to identify intervention components that may be more effective especially for subsets of participants with certain behavioral characteristics. Pooling, when supported by statistical tests, can allow exploratory investigation of potential hypotheses and for the design of future interventions. KeywordsStatistical pooling of studies, Random-effects metaanalysis, Study-level meta-regression, Multilevel meta-regression, Multilevel structural models INTRODUCTION Randomized controlled trials (RCTs) are considered the gold standard experimental study design for establishing the causal effect of an intervention on an outcome of interest. RCTs are usually designed to have high internal validity in addressing specific hypotheses but may have less external validity as their inclusion and exclusion criteria may be very restrictive. Often there are many similar trials addressing the same type of research hypotheses but with different target populations, settings, or outcome measures. Such trials may not evaluate exactly the same intervention, especially in trials of interventions that include combinations of multiple behavioral, social, pharmacological and/or environmental components.

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“…Multilevel interventions include change strategies in two or more levels of influence in the social—ecologic model (Figure 1). 8–10 Interventions with multiple components that address only one level of influence, such as programs that modify aspects of both the food and physical activity environments in schools, are multicomponent, but not multilevel. Multilevel interventions are delivered in more than one setting and researchers are usually (but not always) required to intervene on different people or sets of people in each setting to affect change.…”

Section: Multilevel Interventionsmentioning

confidence: 99%

Multilevel Interventions Targeting Obesity: Research Recommendations for Vulnerable Populations

Stevens

Pratt

Boyington

et al. 2017

American Journal of Preventive Medicine

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Introduction The origins of obesity are complex and multifaceted. To be successful, an intervention aiming to prevent or treat obesity may need to address multiple layers of biological, social, and environmental influences. Methods NIH recognizes the importance of identifying effective strategies to combat obesity, particularly in high-risk and disadvantaged populations with heightened susceptibility to obesity and subsequent metabolic sequelae. To move this work forward, the National Heart, Lung, and Blood Institute, in collaboration with the NIH Office of Behavioral and Social Science Research and NIH Office of Disease Prevention convened a working group to inform research on multilevel obesity interventions in vulnerable populations. The working group reviewed relevant aspects of intervention planning, recruitment, retention, implementation, evaluation, and analysis, and then made recommendations. Results Recruitment and retention techniques used in multilevel research must be culturally appropriate and suited to both individuals and organizations. Adequate time and resources for preliminary work are essential. Collaborative projects can benefit from complementary areas of expertise and shared investigations rigorously pretesting specific aspects of approaches. Study designs need to accommodate the social and environmental levels under study, and include appropriate attention given to statistical power. Projects should monitor implementation in the multiple venues and include a priori estimation of the magnitude of change expected within and across levels. Conclusions The complexity and challenges of delivering interventions at several levels of the social—ecologic model require careful planning and implementation, but hold promise for successful reduction of obesity in vulnerable populations.

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Childhood Obesity Prevention and Treatment Research (COPTR): Interventions addressing multiple influences in childhood and adolescent obesity

Cited by 44 publications

References 40 publications

Prediction of percent body fat measurements in Americans 8 years and older

Prediction of percent body fat measurements in Americans 8 years and older

Statistical methodologies to pool across multiple intervention studies

Multilevel Interventions Targeting Obesity: Research Recommendations for Vulnerable Populations

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