Complex Systems Science: Applications to Health Behavior.

拟议的研究结合了生物生态发展模式的观点与现实主义的评价方法的原则。具体而言，研究将：（1）使用复杂的社会网络方法（例如，多级网络分析）来评估健康的个体、社会和环境决定因素之间的动态（例如，物质使用、危险性行为、心理健康）;（2）调查社交网络的复杂性，因为它们与干预设计有关，以回答关于有效利用社交网络改善健康结果的关键问题。一个成熟的文献表明，社交网络和健康是相互关联的，网络影响健康行为的发展，同时，健康行为影响社会联系的形成。此外，越来越多的证据表明，同龄人可以有效地参与行为干预。然而，为了充分利用社交网络方法改善健康结果的潜力，至关重要的是要超越网络与健康、网络干预和治疗结果之间的简单关联，并开始明确调查这些关联如何在不同环境中转移，以及这些过程通过哪些潜在机制发生。因此，拟议研究的目的是双重的。首先，通过将多层次建模技术与社会网络方法相结合，该研究将回答有关健康结果变化的复杂问题，这些变化取决于个人、社会和环境因素。具体而言，拟议的研究将应用和扩展现有的多层次技术的社会网络分析，以回答关键问题，何时，为谁，在何种程度上，以及在何种情况下，社交网络影响健康。例如，研究将检查个人、同伴和学校层面的差异，以确定这些相互依赖的因素导致健康不公平的方式（例如，（二）青少年。其次，拟议的研究旨在通过超越简单地调查这些方法的有效性来促进我们对基于网络的干预措施的理解，而是专注于解析基于网络的干预措施影响健康结果的潜在机制。例如，该研究将应用并扩展创新方法的传播，以确定这些干预措施对谁，以何种方式以及如何影响健康行为变化。总之，拟议的研究是嵌套在两个免费的方法，行为健康和干预设计;发展的生物生态理论，和现实主义的评价方法。通过这一框架，研究旨在揭示社交网络与健康之间关系的重要变化，同时确定新的干预目标，并扩大我们对网络干预影响行为变化的机制的理解。最后，该研究使用各种现有的数据集来实现上述目标。

The proposed research combines perspectives from bioecological models of development with principles from the realist approach to evaluation. Specifically, the research will: (1) use complex social network methods (e.g., multilevel network analysis) to evaluate the dynamics between individual, social, and environmental determinants of health (e.g., substance use, risky sexual behavior, mental wellbeing); and (2) investigate complexities in social networks as they relate to intervention design in order to answer critical questions regarding the effective leverage of social networks to improve health outcomes. A well-established literature demonstrates that social networks and health are interrelated, such that networks impact the development of health behavior, and simultaneously, health behavior impacts the formation of social connections. Further, growing evidence indicates that peers can be effectively engaged in behavioral interventions. However, in order to fully harness the potential of social network methods to improve health outcomes, it is critically important to look beyond simple associations between networks and health, and network interventions and treatment outcomes, and begin explicitly investigating how these associations transfer across contexts, and through which underlying mechanisms these processes occur. As a result, the aim of the proposed research is two-fold. First, by integrating multilevel modeling techniques with social network methods, the research will answer complex questions regarding variation in health outcomes dependent on individual, social, and environmental factors. Specifically, the proposed research will apply and expand upon existing multilevel techniques for social network analysis in order to answer critical questions regarding when, for whom, to what extent, and under what circumstances social networks impact health. For example, the research will examine variation at the individual, peer, and school level to determine the ways through which these interdependent factors contribute to health inequities (e.g., substance use) among adolescents. Second, the proposed research aims to advance our understanding of network-based interventions by moving beyond simply investigating efficacy of these approaches, and instead focuses on parsing apart the underlying mechanisms through which network-based interventions impact health outcomes. For example, the research will apply and expand upon diffusion of innovation methods in order to determine for whom, in what way, and how these interventions impact health behavior change. In summary, the proposed research is nested within two complimentary approaches to behavioral health and intervention design; the bioecological theory of development, and the realist approach to evaluation. Through this framework, the research aims to uncover important variations in the relationship between social networks and health, while identifying novel intervention targets, and expanding our understanding of the mechanisms through which network interventions impact behavioral change. Lastly, the research uses a variety of existing datasets to accomplish the aforementioned goals.