Longitudinal Networks Core

纵向网络核心

基本信息

  • 批准号:
    9908116
  • 负责人:
  • 金额:
    $ 49.44万
  • 依托单位:
  • 依托单位国家:
    美国
  • 项目类别:
  • 财政年份:
  • 资助国家:
    美国
  • 起止时间:
  • 项目状态:
    未结题

项目摘要

PROJECT ABSTRACT The long-term goal of the Longitudinal Networks Core (LNC) is to enhance the fields of rural substance abuse research and mobile health interventions (mHealth) by offering longitudinal human subjects cohort study capacity to researchers across the Central Plains. To do this, LNC will make use of a cutting-edge cohort tracking and retention software—the Open Dynamic Interaction Network (ODIN) platform—developed by senior Rural Drug Addiction Research Center (RDAR) personnel. This software links data acquisition opportunities brought about by advances in mobile computing and quantified-self technologies with new analytic tools in a seamless, user- friendly, sustainable, and flexible manner. ODIN represents next generation software for both Ecological Momentary Assessment (EMA) and social network data collection—two cutting-edge areas in social and behavioral health research. In addition, ongoing expansion of the ODIN system allows for the integration of emerging mobile biomarker technology into the ODIN data stream, including Actigraph, Emotive EEG, Fitbit, and Empatica e4 wearables. These devices add functionality to collect large streaming data on physiological measures such as cognition, physical activity/sleep, and individual stress via epidermal conductance. Using the ODIN network and EMA platform, LNC will build and maintain a longitudinal Rural Drug User Cohort (RDUC) consisting of 600 drug users in Nebraska, Missouri, Iowa, and Kansas (Aim 1) in service to RDAR projects. The cohort study will involve initial interviews; long-term collection of contextual, continuous time interaction data; hepatitis C testing; and retention for interviews every six months. In this context, RDUC will serve as a strong resource platform and subject pool for a Center projects seeking to collect data on the behaviors, attitudes, biomarkers, and interactions of rural drug users to address critical questions related to rural addiction. Data from this shared cohort will drive the research strategies of Center projects spanning the areas of neuroscience, cognition and decision-making, simulation and epidemiology, epigenetics, and clinical research. In addition, LNC will support the recruitment of RDUC sub-sets for lab-based tests associated with RDAR projects and provide a foundation for future clinical trials for medically assisted addiction recovery drugs. During the period of COBRE Phase 1 funding, LNC will develop a sustainable service capacity for health-related, cohort-based research at the University of Nebraska (Aim 2), marking a significant advancement in regional health research and intervention capacity. To help ensure this, LNC will host an annual mHealth workshop to introduce researchers to the added capacity of ODIN and facilitate the translation of existing rural health interventions and addiction related interventions to the ODIN platform. Contingency plans for meeting initial RDAR projects’ data requirements (in the unlikely event of unexpected delays) are included to ensure overall Center success, and resources are available at the University of Nebraska for the development of a long-term business plan.
项目摘要

项目成果

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Bilal Khan其他文献

Bilal Khan的其他文献

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{{ truncateString('Bilal Khan', 18)}}的其他基金

Mapping the co-evolution of Craving, Affect, Stressors, and Access to Alcohol (CASA) using responsive EMA
使用响应式 EMA 绘制渴望、情感、压力源和酒精获取 (CASA) 的共同演化
  • 批准号:
    10196353
  • 财政年份:
    2021
  • 资助金额:
    $ 49.44万
  • 项目类别:
Mapping the co-evolution of Craving, Affect, Stressors, and Access to Alcohol (CASA) using responsive EMA
使用响应式 EMA 绘制渴望、情感、压力源和酒精获取 (CASA) 的共同演化
  • 批准号:
    10403674
  • 财政年份:
    2021
  • 资助金额:
    $ 49.44万
  • 项目类别:
Longitudinal Networks Core
纵向网络核心
  • 批准号:
    10117084
  • 财政年份:
    2019
  • 资助金额:
    $ 49.44万
  • 项目类别:
Longitudinal Networks Core
纵向网络核心
  • 批准号:
    10377944
  • 财政年份:
    2019
  • 资助金额:
    $ 49.44万
  • 项目类别:

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