Methods Core

研究方法核心提要 我们的中心提出了一种新的以部署为重点的模式，既简化了行为干预 治疗中老年情绪障碍，并改善其在社区的交付。为回应这一事件 为了满足中心和外地的需要，研究方法核心(RMC)将履行以下职能： 1.RMC将为中心新的和正在进行的、独立资助的 干预研究，以确保设计、程序和分析策略的最高质量。 2.区域协调委员会将制定三项倡议中概述的新方法： 举措1.提高T2社区的效率和信息产出的分析方法-- 以有效性研究为基础。该倡议支持该中心的行为干预计划 通过开发分析方法获得多个结果和明确的行为目标：1) 最大限度地提供有关干预结果的信息，反映健康的有意义的方面，并 减少样本量要求；2)估计纵向、连续的间接效应 结果的行为中介；3)增加多次重复的信息产量 通过移动卫生技术和进行比较的能力进行评估。 举措2.整合多个大数据来源以确定需要新技术的人口的方法 干预措施和部署方法以及政策支持。这一倡议是对该中心 以及该领域需要确定有情绪障碍的亚群，目前的医疗保健服务不足 系统。相应地，它将发展：1)异质集成的创新方法 分布式生物医学大数据；2)身心健康多个体识别方法 条件；以及3)描述结果较差的亚组的方法。与 在利益相关者的输入下，这些信息可以为未来的社区干预指明方向。 举措3：将移动技术融入社区干预的新方法，考虑到 说明患者和治疗师的技能组合，以及社区环境的资源。莫比尔县 该中心的行为干预中嵌入了技术，以增加可用于 社区临床医生，并指导他们有针对性地进行治疗。这一倡议是对 中心和外地对老年人和中年人心理健康可用移动技术的需求 消费者，在繁忙的社区治疗环境中可用。 3.评估该中心的研究效率和对该领域的影响。 4.传播方法上的进步和中心提供的其他资源。

RESEARCH METHODS CORE ABSTRACT Our Center proposes a novel deployment focused model that both streamlines behavioral interventions for late- and mid-life mood disorders and improves their delivery in the community. In response to the Center's and the field's needs, the Research Methods Core (RMC) will perform the following functions: 1. The RMC will provide operational support to the Center's new and ongoing, independently funded intervention studies to ensure the highest quality in design, procedures, and analytic strategies. 2. The RMC will develop novel methods outlined in three initiatives: Initiative 1. Analytic methods to increase the efficiency and the information yield of T2 community- based effectiveness studies. This initiative supports the Center's program of behavioral interventions with multiple outcomes and distinct behavioral targets by developing analytic approaches: 1) To maximize information on intervention outcomes reflecting meaningful dimensions of health and to reduce sample size requirements; 2) To estimate the indirect effect of longitudinal, continuous behavioral mediators of outcomes; 3) To increase the information yield of multiple repeated assessments by mobile health technology and the ability to make comparisons. Initiative 2. Approaches to integrating multiple big data sources to identify populations in need of novel interventions and deployment approaches and to policy support. This initiative responds to the Center's and the field's need to identify subgroups with mood disorders underserved by the current health care system. Accordingly, it will develop: 1) Innovative approaches for integration of heterogeneously distributed biomedical big data; 2) Methods for identifying individuals with multiple mental and physical conditions; and 3) Approaches to characterizing subgroups with poor outcomes. Combined with stakeholders' input, this information can chart directions for future community interventions.  Initiative 3: Novel approaches to integrating mobile technology in community interventions taking into account the skill sets of patients and therapists, and the resources of community settings. Mobile technology is embedded in the Center's behavioral interventions to augment information available to community clinicians and to guide them in targeting their sessions. This initiative responds to the Center's and the field's need for mobile technology accessible to older and middle-aged mental health consumers and usable at busy community treatment settings. 3. Evaluate the Center's research productivity and impact on the field. 4. Disseminate methodological advances and other Center generated resources.