Methods Core

方法核心

• 批准号: 10016868
• 负责人: Samprit Banerjee
• 金额: $ 67.76万
• 依托单位: WEILL MEDICAL COLL OF CORNELL UNIV
• 依托单位国家: 美国
• 财政年份: 2017
• 资助国家: 美国
• 起止时间: 2017-09-22 至 2024-08-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10016868
• 关键词: Adult; Behavior Therapy; Behavior assessment; Behavioral; Big Data; Biometry; Clinical; Communities; Complex; Data Analyses; Data Sources; Development; Dimensions; Effectiveness; Effectiveness of Interventions; Elderly; Ensure; Funding; Future; Goals; Grant; Health; Health Policy; Health Services Research; Health Technology; Healthcare; Healthcare Systems; Incubators; Individual; Intervention; Intervention Studies; Joints; Lead; Mediator of activation protein; Medicine; Mental Health; Methodology; Methods; Modeling; Mood Disorders; National Institute of Mental Health; Neurobiology; Outcome; Patients; Phenotype; Policies; Population; Procedures; Productivity; Psyche structure; Psychiatry; Psychology; Public Health; Public Health Informatics; Research; Research Methodology; Resources; Risk; Sample Size; Series; Site; Subgroup; Testing; Time; Work; advanced analytics; analytical method; base; big biomedical data; cohort; community intervention; community setting; data management; design; health care economics; health economics; improved; innovation; mHealth; method development; middle age; mobile computing; novel; novel strategies; programs; response; skills; therapy development; uptake

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.

研究方法核心摘要 我们的中心提出了一个新的部署重点模型，既简化了行为干预 治疗晚期和中年情绪障碍，并改善社区的服务。响应于 研究方法核心（RMC）将根据中心和实地的需要履行以下职能： 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. RMC将开发三项举措中概述的新方法： 倡议1.提高T2群落效率和信息产量的分析方法- 基于有效性研究。该倡议支持该中心的行为干预方案， 通过开发分析方法，实现多个结果和不同的行为目标：1） 最大限度地提供关于反映有意义的健康层面的干预结果的信息， 减少样本量要求; 2）估计纵向、连续 结果的行为中介; 3）增加多个重复的信息产量 通过移动的医疗技术进行评估和进行比较的能力。 倡议2.整合多个大数据源以确定需要新的药物的人群的方法 干预措施和部署方法以及政策支持。这一举措响应了该中心的 以及该领域需要确定当前医疗保健服务不足的情绪障碍亚组 系统因此，它将开发：1）整合异质性的创新方法 分布式生物医学大数据; 2）用于识别具有多种精神和身体疾病的个体的方法 条件;和3）表征结果不佳的亚组的方法。结合 这些信息可以为未来的社区干预指明方向。 举措3：将移动的技术纳入社区干预措施的新方法， 考虑到病人和治疗师的技能，以及社区环境的资源。移动的 技术被嵌入到中心的行为干预，以增加信息， 社区临床医生，并指导他们在针对他们的会议。这一举措是为了响应 中心和现场对移动的技术的需求可用于老年人和中年人的心理健康 消费者和可用于忙碌社区治疗设置。 3.评估中心的研究生产力和对该领域的影响。 4.传播方法的进步和其他中心产生的资源。