A systems-oriented multilevel framework for addressing obesity in the 21st century.
Bibliographic record
Abstract
Effective or sustainable prevention strategies for obesity, particularly in youths, have been elusive since the recognition of obesity as a major public health issue 2 decades ago. Although many advances have been made with regard to the basic biology of adiposity and behavioral modifications at the individual level, little success has been achieved in either preventing further weight gain or maintaining weight loss on a population level (1). To a great extent, this is the result of the complex task of trying to change the way people eat, move, and live, and sustaining those changes over time. The most immediate cause of obesity is an imbalance of energy intake and energy expenditure in the body. This energy imbalance, on the magnitude seen in today's population, arises from the complex interactions of biological susceptibilities and socioenvironmental changes (2). Evidence in behavioral economics suggests that these powerful biological and contextual forces often place eating and exercise behavior beyond an individual's rational control (3). Therefore, the solution to the obesity epidemic lies in policies and interventions that alter those contextual features, taking individual biology and preferences into account. Historically, obesity research has been conducted within individual disciplines. Now, for both scientific inquiry and for public policies, obesity should be framed as a complex system in which behavior is affected by multiple individual-level factors and socioenvironmental factors (ie, factors related to the food, physical, cultural, or economic environment that enable or constrain human behavior, or both). These factors are heterogeneous and interdependent, and they interact dynamically (4). Because of the complex system that affects obesity, researchers need to use a systems-oriented approach to address the multiple factors and levels. Whereas multidisciplinary research consists of teams with different expertise that can contribute to the understanding of particular aspects of a larger research question, truly cross-disciplinary research asks a priori questions and poses hypotheses that cut across disciplines and across levels of influence. For example, how do biological mechanisms of energy metabolism react to or how are they affected by different features of the built, social, or economic environment to produce a given distribution of eating or physical activity? How do these conditions enable or constrain eating and physical activity, and how are they embodied in biological systems to affect these behaviors? In October 2007, the Eunice Kennedy Shriver National Institute of Child Health and Human Development (NICHD) convened the international conference Beyond Individual Behavior: Multidimensional Research in Obesity Linking Biology to Society. The goal was to create a climate of training, funding, and academic and institutional support for obesity research that will offer sustainable solutions to the obesity problem. Participants hoped to bridge the factors that influence obesity-related behaviors at the macro level (typically policies that shape and govern the food, physical, social, and economic environments in which we live) and the micro level (typically variables within people or their immediate surroundings that influence health outcomes). The conference was supported by the National Institutes of Health (National Cancer Institute; National Institute of Diabetes and Digestive and Kidney Diseases; National Heart, Lung, and Blood Institute; Division of Nutrition Research Coordination, Office of Behavioral and Social Sciences Research; and Office of Disease Prevention), the Canadian Institutes of Health Research (Institute of Nutrition, Metabolism, and Diabetes), and the Centers for Disease Control and Prevention. The content of this 3-day conference was designed to explicate the scientific foundation of this multilevel approach, generate research questions that apply to all disciplines, consider different intervention models, and discuss methods needed for the design and analysis of systems-oriented, multilevel studies (5). The essential elements of this multilevel agenda are framing obesity as a complex systems problem; encouraging cross-disciplinary questions and hypotheses; focusing on structural interventions (ie, modifications to the environment or policies); building capacity for multilevel research and action; and taking a global perspective.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.006 | 0.004 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.003 | 0.013 |
| Scholarly communication | 0.007 | 0.007 |
| Open science | 0.004 | 0.007 |
| Research integrity | 0.005 | 0.006 |
| Insufficient payload (model declined to judge) | 0.015 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".