Targeted self-regulation interventions in low-income children: Clinical trial results and implications for health behavior change.

Self-regulation, known as the ability to harness cognitive, emotional, and motivational resources to achieve goals, is hypothesized to contribute to health behaviors across the lifespan. Enhancing self-regulation early in life may increase positive health outcomes. During pre-adolescence, children assume increased autonomy in health behaviors (e.g., eating; physical activity), many of which involve self-regulation. This article presents results from a clinical trial (NCT03060863) that used a factorial design to test behavioral interventions designed to enhance self-regulation, specifically targeting executive functioning, emotion regulation, future-oriented thinking, and approach biases. Participants were 118 children (9-12 years of age, M = 10.2 years) who had a history of living in poverty. They were randomized to receive up to four interventions that were delivered via home visits. Self-regulation was assayed using behavioral tasks, observations, interviews, and parent- and child-report surveys. Results were that self-regulation targets were reliably assessed and that interventions were delivered with high fidelity. Intervention effect sizes were very small to moderate (d range = .02-.65, median = .14), and most were not statistically significant. Intercorrelation analyses indicated that associations between measures within each target varied based on the self-regulation target evaluated. Results are discussed with regard to the role of self-regulation-focused interventions in child health promotion. Implications of findings are reviewed for informing next steps in behavioral self-regulation interventions among children from low-income backgrounds.

Precision Health: The Role of the Social and Behavioral Sciences in Advancing the Vision.

BACKGROUND: In 2015, Collins and Varmus articulated a vision for precision medicine emphasizing molecular characterization of illness to identify actionable biomarkers to support individualized treatment. Researchers have argued for a broader conceptualization, precision health. Precision health is an ambitious conceptualization of health, which includes dynamic linkages between research and practice as well as medicine, population health, and public health. The goal is a unified approach to match a full range of promotion, prevention, diagnostic, and treatment interventions to fundamental and actionable determinants of health; to not just address symptoms, but to directly target genetic, biological, environmental, and social and behavioral determinants of health. PURPOSE: The purpose of this paper is to elucidate the role of social and behavioral sciences within precision health. MAIN BODY: Recent technologies, research frameworks, and methods are enabling new approaches to measure, intervene, and conduct social and behavioral science research. These approaches support three opportunities in precision health that the social and behavioral sciences could colead including: (a) developing interventions that continuously "tune" to each person's evolving needs; (b) enhancing and accelerating links between research and practice; and (c) studying mechanisms of change in real-world contexts. There are three challenges for precision health: (a) methods of knowledge organization and curation; (b) ethical conduct of research; and (c) equitable implementation of precision health. CONCLUSIONS: Precision health requires active coleadership from social and behavioral scientists. Prior work and evidence firmly demonstrate why the social and behavioral sciences should colead with regard to three opportunity and three challenge areas.

Adolescent Interventions to Manage Self-Regulation in Type 1 Diabetes (AIMS-T1D): randomized control trial study protocol.

BACKGROUND: Self-regulation (SR), or the capacity to control one's thoughts, emotions, and behaviors in order to achieve a desired goal, shapes health outcomes through many pathways, including supporting adherence to medical treatment regimens. Type 1 Diabetes (T1D) is one specific condition that requires SR to ensure adherence to daily treatment regimens that can be arduous and effortful (e.g., monitoring blood glucose). Adolescents, in particular, have poor adherence to T1D treatment regimens, yet it is essential that they assume increased responsibility for managing their T1D as they approach young adulthood. Adolescence is also a time of rapid changes in SR capacity and thus a compelling period for intervention. Promoting SR among adolescents with T1D may thus be a novel method to improve treatment regimen adherence. The current study tests a behavioral intervention to enhance SR among adolescents with T1D. SR and T1D medical regimen adherence will be examined as primary and secondary outcomes, respectively. METHODS: We will use a randomized control trial design to test the impact of a behavioral intervention on three SR targets: Executive Functioning (EF), Emotion Regulation (ER), and Future Orientation (FO); and T1D medical regimen adherence. Adolescents with T1D (n = 94) will be recruited from pediatric endocrinology clinics and randomly assigned to treatment or control group. The behavioral intervention consists of working memory training (to enhance EF), biofeedback and relaxation training (to enhance ER), and episodic future thinking training (to enhance FO) across an 8-week period. SR and treatment regimen adherence will be assessed at pre- and post-test using multiple methods (behavioral tasks, diabetes device downloads, self- and parent-report). We will use an intent-to-treat framework using generalized linear mixed models to test our hypotheses that: 1) the treatment group will demonstrate greater improvements in SR than the control group, and 2) the treatment group will demonstrate better treatment regimen adherence outcomes than the control group. DISCUSSION: If successful, SR-focused behavioral interventions could improve health outcomes among adolescents with T1D and have transdiagnostic implications across multiple chronic conditions requiring treatment regimen adherence. TRIAL REGISTRATION: ClinicalTrials.gov: NCT03688919; registered September 28, 2018.

Advancing Health Policy and Program Research in Diabetes: Findings from the Natural Experiments for Translation in Diabetes (NEXT-D) Network.

PURPOSE OF REVIEW: To advance our understanding of the impacts of policies and programs aimed at improving detection, engagement, prevention, and clinical diabetes management in the USA, we synthesized findings from a network of studies that used natural experiments to evaluate diabetes health policies and programs. FINDINGS: Studies from the Natural EXperiments for Translation in Diabetes (NEXT-D) network used rigorous longitudinal quasi-experimental study designs (e.g., interrupted time series) and analytical methods (e.g., difference-in-differences) to augment causal inference. Investigators partnered with health system stakeholders to evaluate whether glucose testing rates changed from before-to-after clinic interventions (e.g., integrating electronic screening decision prompts in New York City) or employer programs (e.g., targeted messaging and waiving copayments for at-risk employees). Other studies examined participation and behavior change in low- (e.g., wellness coaching) or high-intensity lifestyle modification programs (e.g., diabetes prevention program-like interventions) offered by payers or employers. Lastly, studies assessed how employer health insurance benefits impacted healthcare utilization, adherence, and outcomes among people with diabetes. NEXT-D demonstrated that low-intensity interventions to facilitate glucose testing and enhance engagement in lifestyle modification were associated with small improvements in weight but large improvements in screening and testing when supported by electronic health record-based decision-support. Regarding high-intensity diabetes prevention program-like lifestyle programs offered by payers or employers, enrollment was modest and led to weight loss and marginally lower short-term health expenditures. Health plans that incentivize patient behaviors were associated with increases in medication adherence. Meanwhile, shifting patients to high-deductible health plans was associated with no change in medication use and preventive screenings, but patients with diabetes delayed accessing healthcare for acute complications (e.g., cellulitis). Findings were more pronounced among lower-income patients, who experienced increased rates and acuity of emergency department visits for diabetes complications and other high-severity conditions. Findings from NEXT-D studies provide informative data that can guide programs and policies to facilitate detection, prevention, and treatment of diabetes in practice.

Spatial capture-mark-resight estimation of animal population density.

Sightings of previously marked animals can extend a capture-recapture dataset without the added cost of capturing new animals for marking. Combined marking and resighting methods are therefore an attractive option in animal population studies, and there exist various likelihood-based non-spatial models, and some spatial versions fitted by Markov chain Monte Carlo sampling. As implemented to date, the focus has been on modeling sightings only, which requires that the spatial distribution of pre-marked animals is known. We develop a suite of likelihood-based spatial mark-resight models that either include the marking phase ("capture-mark-resight" models) or require a known distribution of marked animals (narrow-sense "mark-resight"). The new models sacrifice some information in the covariance structure of the counts of unmarked animals; estimation is by maximizing a pseudolikelihood with a simulation-based adjustment for overdispersion in the sightings of unmarked animals. Simulations suggest that the resulting estimates of population density have low bias and adequate confidence interval coverage under typical sampling conditions. Further work is needed to specify the conditions under which ignoring covariance results in unacceptable loss of precision, or to modify the pseudolikelihood to include that information. The methods are applied to a study of ship rats Rattus rattus using live traps and video cameras in a New Zealand forest, and to previously published data.

Primary Care Behavioral Health (PCBH) Model Research: Current State of the Science and a Call to Action.

The Primary Care Behavioral Health (PCBH) model of service delivery is being used increasingly as an effective way to integrate behavioral health services into primary care. Despite its growing popularity, scientifically robust research on the model is lacking. In this article, we provide a qualitative review of published PCBH model research on patient and implementation outcomes. We review common barriers and potential solutions for improving the quantity and quality of PCBH model research, the vital data that need to be collected over the next 10 years, and how to collect those data.

Advancing digital health equity: Directions for behavioral and social science research.

The field of digital health is evolving rapidly and encompasses a wide range of complex and changing technologies used to support individual and population health. The COVID-19 pandemic has augmented digital health expansion and significantly changed how digital health technologies are used. To ensure that these technologies do not create or exacerbate existing health disparities, a multi-pronged and comprehensive research approach is needed. In this commentary, we outline five recommendations for behavioral and social science researchers that are critical to promoting digital health equity. These recommendations include: (i) centering equity in research teams and theoretical approaches, (ii) focusing on issues of digital health literacy and engagement, (iii) using methods that elevate perspectives and needs of underserved populations, (iv) ensuring ethical approaches for collecting and using digital health data, and (v) developing strategies for integrating digital health tools within and across systems and settings. Taken together, these recommendations can help advance the science of digital health equity and justice.

The field of digital health is quickly growing and changing. Digital health technologies have the potential to increase access to health-related information and healthcare and improve wellbeing, but it is important that those technologies don't widen existing health disparities or create new ones. Behavioral and social science researchers have a key role to play in centering equity in their research teams and theoretical approaches, focusing on key barriers to access, uptake, and usage, studying digital health in ways that elevate the voices and needs of historically underserved groups, being thoughtful about how digital health data are collected and used, and making sure that digital health tools are designed to be used in real-world settings.

From ideas to interventions: A review and comparison of frameworks used in early phase behavioral translation research.

Developing and testing more effective health-related behavioral interventions is critical to making progress in improving disease prevention and treatment. One way to achieve this goal is to use a systematic and progressive framework that outlines the steps needed to translate theories, findings, and basic understandings about human behavior into risk factor and disease management or mitigation strategies. Although several frameworks and process models have been designed to inform the development and optimization of health-related behavioral interventions, little guidance is available to compare key aspects of these models, clarify their common and unique features, and aid in selecting the best approach for a specific research question. This article describes the major frameworks that focus on early phase translation-that is, approaches that address the design and optimization of behavioral interventions before testing in Phase III efficacy trials. Differences between and common features of these models are described, opportunities for combining frameworks to maximize their impact are noted, and guidance is provided to enable investigators to choose the most useful model(s) when designing and optimizing health-related behavioral interventions. The goal of this article is to promote the consistent use of frameworks that encourage a systematic, progressive approach to behavioral intervention development and testing as one way to encourage the creation of well-characterized, optimized, and potentially more effective health-related behavioral interventions. (PsycInfo Database Record (c) 2022 APA, all rights reserved).

Behavioral and social science in support of SARS-CoV-2 vaccination: National Institutes of Health initiatives.

Control of the COVID-19 pandemic relies heavily on behavioral mitigation strategies such as physical distancing, hand washing and mask wearing. Even with the availability of SARS-CoV-2 vaccines, the extraordinary effort to distribute the vaccines must be paired with continued adherence to behavioral recommendations as well as vaccine confidence. To facilitate rapid and equitable uptake of the vaccines, there is a need for responsive, trustworthy, and evidence-informed communication about vaccination, enhanced trust in science, and engaging populations disproportionately affected by the pandemic. Efforts to address vaccine hesitancy and increase vaccine confidence will address the emerging gaps between vaccine availability and actual vaccination. Although these gaps are attributable, in part, to challenges with logistics and access, social and behavioral drivers of vaccination decision making also have a significant role in vaccination uptake. As federal, state, and local health and public health agencies coordinate vaccine dissemination, there will be a continuous need to adapt to an evolving landscape of SARS-CoV-2 vaccines, new scientific information, and the spread of COVID-19- and vaccine-related misinformation. Facilitating widespread vaccination and maintaining a focus on equity requires thoughtful and compassionate approaches to reach and address the needs of those who are disproportionately affected by the pandemic such as underserved, vulnerable, and racial/ethnic minority populations. This commentary focuses on several National Institutes of Health initiatives that are supporting behavioral and social science research to address SARS-CoV-2 vaccine communication and increase the uptake of vaccination. We conclude with implications for future research.

Warming Arctic summers unlikely to increase productivity of shorebirds through renesting.

Climate change in the Arctic is leading to earlier summers, creating a phenological mismatch between the hatching of insectivorous birds and the availability of their invertebrate prey. While phenological mismatch would presumably lower the survival of chicks, climate change is also leading to longer, warmer summers that may increase the annual productivity of birds by allowing adults to lay nests over a longer period of time, replace more nests that fail, and provide physiological relief to chicks (i.e., warmer temperatures that reduce thermoregulatory costs). However, there is little information on how these competing ecological processes will ultimately impact the demography of bird populations. In 2008 and 2009, we investigated the survival of chicks from initial and experimentally-induced replacement nests of arcticola Dunlin (Calidris alpina) breeding near Utqiagvik, Alaska. We monitored survival of 66 broods from 41 initial and 25 replacement nests. Based on the average hatch date of each group, chick survival (up to age 15 days) from replacement nests (Si = 0.10; 95% CI = 0.02-0.22) was substantially lower than initial nests (Si = 0.67; 95% CI = 0.48-0.81). Daily survival rates were greater for older chicks, chicks from earlier-laid clutches, and during periods of greater invertebrate availability. As temperature was less important to daily survival rates of shorebird chicks than invertebrate availability, our results indicate that any physiological relief experienced by chicks will likely be overshadowed by the need for adequate food. Furthermore, the processes creating a phenological mismatch between hatching of shorebird young and invertebrate emergence ensures that warmer, longer breeding seasons will not translate into abundant food throughout the longer summers. Thus, despite having a greater opportunity to nest later (and potentially replace nests), young from these late-hatching broods will likely not have sufficient food to survive. Collectively, these results indicate that warmer, longer summers in the Arctic are unlikely to increase annual recruitment rates, and thus unable to compensate for low adult survival, which is typically limited by factors away from the Arctic-breeding grounds.

Climate change threatens polar bear populations: a stochastic demographic analysis.

The polar bear (Ursus maritimus) depends on sea ice for feeding, breeding, and movement. Significant reductions in Arctic sea ice are forecast to continue because of climate warming. We evaluated the impacts of climate change on polar bears in the southern Beaufort Sea by means of a demographic analysis, combining deterministic, stochastic, environment-dependent matrix population models with forecasts of future sea ice conditions from IPCC general circulation models (GCMs). The matrix population models classified individuals by age and breeding status; mothers and dependent cubs were treated as units. Parameter estimates were obtained from a capture-recapture study conducted from 2001 to 2006. Candidate statistical models allowed vital rates to vary with time and as functions of a sea ice covariate. Model averaging was used to produce the vital rate estimates, and a parametric bootstrap procedure was used to quantify model selection and parameter estimation uncertainty. Deterministic models projected population growth in years with more extensive ice coverage (2001-2003) and population decline in years with less ice coverage (2004-2005). LTRE (life table response experiment) analysis showed that the reduction in lambda in years with low sea ice was due primarily to reduced adult female survival, and secondarily to reduced breeding. A stochastic model with two environmental states, good and poor sea ice conditions, projected a declining stochastic growth rate, log lambdas, as the frequency of poor ice years increased. The observed frequency of poor ice years since 1979 would imply log lambdas approximately - 0.01, which agrees with available (albeit crude) observations of population size. The stochastic model was linked to a set of 10 GCMs compiled by the IPCC; the models were chosen for their ability to reproduce historical observations of sea ice and were forced with "business as usual" (A1B) greenhouse gas emissions. The resulting stochastic population projections showed drastic declines in the polar bear population by the end of the 21st century. These projections were instrumental in the decision to list the polar bear as a threatened species under the U.S. Endangered Species Act.

Survival and breeding of polar bears in the southern Beaufort Sea in relation to sea ice.

1. Observed and predicted declines in Arctic sea ice have raised concerns about marine mammals. In May 2008, the US Fish and Wildlife Service listed polar bears (Ursus maritimus) - one of the most ice-dependent marine mammals - as threatened under the US Endangered Species Act. 2. We evaluated the effects of sea ice conditions on vital rates (survival and breeding probabilities) for polar bears in the southern Beaufort Sea. Although sea ice declines in this and other regions of the polar basin have been among the greatest in the Arctic, to date population-level effects of sea ice loss on polar bears have only been identified in western Hudson Bay, near the southern limit of the species' range. 3. We estimated vital rates using multistate capture-recapture models that classified individuals by sex, age and reproductive category. We used multimodel inference to evaluate a range of statistical models, all of which were structurally based on the polar bear life cycle. We estimated parameters by model averaging, and developed a parametric bootstrap procedure to quantify parameter uncertainty. 4. In the most supported models, polar bear survival declined with an increasing number of days per year that waters over the continental shelf were ice free. In 2001-2003, the ice-free period was relatively short (mean 101 days) and adult female survival was high (0.96-0.99, depending on reproductive state). In 2004 and 2005, the ice-free period was longer (mean 135 days) and adult female survival was low (0.73-0.79, depending on reproductive state). Breeding rates and cub litter survival also declined with increasing duration of the ice-free period. Confidence intervals on vital rate estimates were wide. 5. The effects of sea ice loss on polar bears in the southern Beaufort Sea may apply to polar bear populations in other portions of the polar basin that have similar sea ice dynamics and have experienced similar, or more severe, sea ice declines. Our findings therefore are relevant to the extinction risk facing approximately one-third of the world's polar bears.

Childhood Obesity Evidence Base Project: Methods for Taxonomy Development for Application in Taxonomic Meta-Analysis.

Meta-analysis has been used to examine the effectiveness of childhood obesity prevention efforts, yet traditional conventional meta-analytic methods restrict the kinds of studies included, and either narrowly define mechanisms and agents of change, or examine the effectiveness of whole interventions as opposed to the specific actions that comprise interventions. Taxonomic meta-analytic methods widen the aperture of what can be included in a meta-analysis data set, allowing for inclusion of many types of interventions and study designs. The National Collaborative on Childhood Obesity Research Childhood Obesity Evidence Base (COEB) project focuses on interventions intended to prevent childhood obesity in children 2-5 years old who have an outcome measure of BMI. The COEB created taxonomies, anchored in the Social Ecological Model, which catalog specific outcomes, intervention components, intended recipients, and contexts of policies, initiatives, and interventions conducted at the individual, interpersonal, organizational, community, and societal level. Taxonomies were created by discovery from the literature itself using grounded theory. This article describes the process used for a novel taxonomic meta-analysis of childhood obesity prevention studies between the years 2010 and 2019. This method can be applied to other areas of research, including obesity prevention in additional populations.

Childhood Obesity Evidence Base Project: A Systematic Review and Meta-Analysis of a New Taxonomy of Intervention Components to Improve Weight Status in Children 2-5 Years of Age, 2005-2019.

Objective: To evaluate the efficacy of childhood obesity interventions and conduct a taxonomy of intervention components that are most effective in changing obesity-related health outcomes in children 2-5 years of age. Methods: Comprehensive searches located 51 studies from 18,335 unique records. Eligible studies: (1) assessed children aged 2-5, living in the United States; (2) evaluated an intervention to improve weight status; (3) identified a same-aged comparison group; (4) measured BMI; and (5) were available between January 2005 and August 2019. Coders extracted study, sample, and intervention characteristics. Effect sizes [ESs; and 95% confidence intervals (CIs)] were calculated by using random-effects models. Meta-regression was used to determine which intervention components explain variability in ESs. Results: Included were 51 studies evaluating 58 interventions (N = 29,085; mean age = 4 years; 50% girls). Relative to controls, children receiving an intervention had a lower BMI at the end of the intervention (g = 0.10, 95% CI = 0.02-0.18; k = 55) and at the last follow-up (g = 0.17, 95% CI = 0.04-0.30; k = 14; range = 18-143 weeks). Three intervention components moderated efficacy: engage caregivers in praise/encouragement for positive health-related behavior; provide education about the importance of screen time reduction to caregivers; and engage pediatricians/health care providers. Conclusions: Early childhood obesity interventions are effective in reducing BMI in preschool children. Our findings suggest that facilitating caregiver education about the importance of screen time reduction may be an important strategy in reducing early childhood obesity.

Smokeless tobacco cessation in military personnel: a randomized controlled trial.

INTRODUCTION: Military personnel are twice as likely as civilians to use smokeless tobacco (ST). This study evaluated the efficacy of a minimal-contact ST cessation program in military personnel. METHODS: Participants were recruited from 24 military dental clinics across the United States during annual dental examinations. Participants were 785 active-duty military personnel who were randomly assigned to receive a minimal-contact behavioral treatment (n = 392) or usual care (n = 393). The behavioral treatment included an ST cessation manual, a videotape cessation guide tailored for military personnel, and three 15-min telephone counseling sessions using motivational interviewing methods. Usual care consisted of standard procedures that are part of the annual dental examination, including recommendations to quit using ST and referral to extant local tobacco cessation programs. Participants were assessed at 3 and 6 months after enrollment. RESULTS: Participants in the ST cessation program were significantly more likely to be abstinent from all tobacco, as assessed by repeated point prevalence at both 3 and 6 months (25.0%), and were significantly more likely to be abstinent from ST use for 6 months, as assessed by prolonged abstinence (16.8%), compared with participants in usual care (7.6% and 6.4%, respectively). DISCUSSION: These results indicate that a minimal-contact behavioral treatment can significantly reduce ST use in military personnel and has the potential for widespread dissemination. If ST users were identified in dental visits and routinely referred to telephone counseling, this could have a substantial benefit for the health and well-being of military personnel.

Using patient reported outcomes in diabetes research and practice: Recommendations from a national workshop.

The National Institute of Diabetes and Digestive and Kidney Diseases (NIDDK) and the American Diabetes Association (ADA) Co-Sponsored the workshop, Using Patient Reported Outcomes in Diabetes Research and Practice. The goal of the workshop was to identify PRO research priorities for those living with type 1 or type 2 diabetes, discuss considerations for use of disease specific versus generic measures, as well as outline research priorities to meet key end-user needs for assessing PROs for diabetes researchers, clinicians/hospital systems, patients/caregivers, and regulators. Here, we summarize the conclusions and recommendations from the workshop.

Team-Based Care to Improve Diabetes Management: A Community Guide Meta-analysis.

CONTEXT: Team-based care has been increasingly used to deliver care for patients with chronic conditions, but its effectiveness for managing diabetes has not been systematically assessed. EVIDENCE ACQUISITION: RCTs were identified from two sources: a high-quality, broader review comparing 11 quality improvement strategies for diabetes management (database inception to July 2010), and an updated search using the same search strategy (July 2010-October 2015). EVIDENCE SYNTHESIS: Thirty-five studies were included in the current review; a majority focused on patients with Type 2 diabetes. Teams included patients, their primary care providers, and one or two additional healthcare professionals (most often nurses or pharmacists). Random effect meta-analysis showed that, compared with controls, team-based care was associated with greater reductions in blood glucose levels (-0.5% in HbA1c, 95% CI= -0.7, -0.3) and greater improvements in blood pressure and lipid levels. Interventions also increased the proportion of patients who reached target blood glucose, blood pressure, and lipid levels, based on American Diabetes Association guidelines available at the time. Data analysis was completed in 2016. CONCLUSIONS: For patients with Type 2 diabetes, team-based care improves blood glucose, blood pressure, and lipid levels.

Weight management using the internet a randomized controlled trial.

BACKGROUND: Most weight-loss research targets obese individuals who desire large weight reductions. However, evaluation of weight-gain prevention in overweight individuals is also critical as most Americans become obese as a result of a gradual gain of 1-2 pounds per year over many years. METHOD: This study evaluated the efficacy of an Internet-based program for weight-loss and weight-gain prevention with a two-group, prospective, randomized controlled trial. A military medical research center with a population of 17,000 active-duty military personnel supplied 446 overweight individuals (222 men; 224 women) with a mean age of 34 years and a mean BMI of 29. Recruitment and study participation occurred 2003-2005 and data were analyzed in 2006. Participants were randomly assigned to receive the 6-month behavioral Internet treatment (BIT, n=227) or usual care (n=224). Change in body weight, BMI, percent body fat, and waist circumference; presented as group by time interactions, were measured. RESULTS: After 6 months, completers who received BIT lost 1.3 kg while those assigned to usual care gained 0.6 kg (F((df=366))=24.17; I<0.001). Results were similar for the intention-to-treat model. BIT participants also had significant changes in BMI (-0.5 vs +0.2 kg/m(2); F((df=366))=24.58); percent body fat (-0.4 vs +0.6%; F((df=366))=10.45); and waist circumference (-2.1 vs -0.4 cm; F((df=366))=17.09); p<0.001 for all. CONCLUSIONS: Internet-based weight-management interventions result in small amounts of weight loss, prevent weight gain, and have potential for widespread dissemination as a population health approach. TRIAL REGISTRATION: NCT00417599.

Obesity classification in military personnel: a comparison of body fat, waist circumference, and body mass index measurements.

OBJECTIVE: The purpose of this study was to evaluate obesity classifications from body fat percentage (BF%), body mass index (BMI), and waist circumference (WC). METHODS: A total of 451 overweight/obese active duty military personnel completed all three assessments. RESULTS: Most were obese (men, 81%; women, 98%) using National Institutes of Health (NIH) BF% standards (men, >25%; women, >30%). Using the higher World Health Organization (WHO) BF >35% standard, 86% of women were obese. BMI (55.5% and 51.4%) and WC (21.4% and 31.9%) obesity rates were substantially lower for men and women, respectively (p < 0.05). BMI/WC were accurate discriminators for BF% obesity (theta for all comparisons >0.75, p < 0.001). Optimal cutoff points were lower than NIH/WHO standards; WC = 100 cm and BMI = 29 maximized sensitivity and specificity for men, and WC = 79 cm and BMI = 25.5 (NIH) or WC = 83 cm and BMI = 26 (WHO) maximized sensitivity and specificity for women. CONCLUSIONS: Both WC and BMI measures had high rates of false negatives compared to BF%. However, at a population level, WC/BMI are useful obesity measures, demonstrating fair-to-high discriminatory power.

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 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.