Adaptive Interventions for Problem Drinkers

针对饮酒问题的适应性干预措施

基本信息

项目摘要

DESCRIPTION (provided by applicant): Problem drinkers (PDs) represent a majority of the estimated 32 million Americans with alcohol problems that spans a spectrum of severity from individuals who drink excessively and experience of occasional negative consequences to those with moderate AD and intact psychosocial functioning. PDs can benefit from relatively brief treatment that could be delivered in mainstream healthcare, but less than 5% receive such care. In addition, PD treatment is only modestly effective, and there is a surprising absence of empirical research to guide PD treatment selection. Adaptive Interventions (AI) are a novel approach to treatment development that may have significant advantages over fixed treatments in improving efficacy and fostering adoption of EBPs in mainstream healthcare. Over the last decade, important advances have been made in AI development methods including Sequential Multiple Assignment Randomized Trials (SMART) and control engineering (CE) designs. These advances have yielded important discoveries in the treatment of other chronic illnesses but are just beginning to be applied to AUD. This study proposes to combine SMART, CE, and ecological momentary assessment to develop an AI for PD that can be used in mainstream healthcare. If study aims are achieved, a set of empirically-derived decision support tools will be created to guide AUD care similar to tools that exist for other chronic diseases. In addition, new knowledge will be gained about MOBC of AUD that can guide future AUD treatment research. Finally, important progress will be made in methods that capitalize on the remarkable advances in sensor technologies, advanced mathematics, and engineering to create a new type of tailored, near-real time feedback, adaptive behavior therapies
描述(由申请人提供):估计有 3200 万有酒精问题的美国人中的大多数都是问题饮酒者 (PD),这些问题的严重程度从过度饮酒到偶尔经历负面后果的个人,再到患有中度 AD 和完整心理社会功能的人。 PD 可以从主流医疗保健中提供的相对简短的治疗中受益,但只有不到 5% 的人接受此类护理。此外,PD 治疗的效果有限,而且令人惊讶的是缺乏指导 PD 治疗选择的实证研究。适应性干预 (AI) 是一种新的治疗开发方法,在提高疗效和促进 EBP 在主流医疗保健中的采用方面可能比固定治疗具有显着优势。在过去的十年中,人工智能开发方法取得了重要进展,包括序贯多重分配随机试验(SMART)和控制工程(CE)设计。这些进展在其他慢性疾病的治疗中取得了重要发现,但刚刚开始应用于 AUD。本研究建议结合 SMART、CE 和生态瞬时评估来开发可用于主流医疗保健的 PD 人工智能。如果研究目标得以实现,将会出现一套基于经验的决策支持工具 创建的目的是指导 AUD 护理,类似于其他慢性病的现有工具。此外,还将获得有关 AUD 的 MOBC 的新知识,可以指导未来的 AUD 治疗研究。最后,利用传感器技术、高等数学和工程学的显着进步的方法将取得重要进展,以创建一种新型的定制、近实时反馈、适应性行为疗法

项目成果

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JON MORGENSTERN其他文献

JON MORGENSTERN的其他文献

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{{ truncateString('JON MORGENSTERN', 18)}}的其他基金

Neural and Mobile Assessment of Behavior Change Among Problem Drinkers
对问题饮酒者行为变化的神经和移动评估
  • 批准号:
    9618601
  • 财政年份:
    2017
  • 资助金额:
    $ 71.16万
  • 项目类别:
Neural and Mobile Assessment of Behavior Change Among Problem Drinkers
对问题饮酒者行为变化的神经和移动评估
  • 批准号:
    9246213
  • 财政年份:
    2017
  • 资助金额:
    $ 71.16万
  • 项目类别:
Neural and Mobile Assessment of Behavior Change Among Problem Drinkers
对问题饮酒者行为变化的神经和移动评估
  • 批准号:
    10321942
  • 财政年份:
    2017
  • 资助金额:
    $ 71.16万
  • 项目类别:
Adaptive Interventions for Problem Drinkers
针对饮酒问题的适应性干预措施
  • 批准号:
    9649148
  • 财政年份:
    2015
  • 资助金额:
    $ 71.16万
  • 项目类别:
Adaptive Interventions for Problem Drinkers
针对饮酒问题的适应性干预措施
  • 批准号:
    9235209
  • 财政年份:
    2015
  • 资助金额:
    $ 71.16万
  • 项目类别:
New York State Health Home Impact on HIV Treatment Cascade
纽约州健康之家对艾滋病毒治疗级联的影响
  • 批准号:
    8771003
  • 财政年份:
    2014
  • 资助金额:
    $ 71.16万
  • 项目类别:
New York State Health Home Impact on HIV Treatment Cascade
纽约州健康之家对艾滋病毒治疗级联的影响
  • 批准号:
    9094485
  • 财政年份:
    2014
  • 资助金额:
    $ 71.16万
  • 项目类别:
The Impact of Health Homes in New York State on People with Substance Use Disorde
纽约州健康之家对药物滥用患者的影响
  • 批准号:
    8534528
  • 财政年份:
    2013
  • 资助金额:
    $ 71.16万
  • 项目类别:
The Impact of Health Homes in New York State on People with Substance Use Disorde
纽约州健康之家对药物滥用患者的影响
  • 批准号:
    8823037
  • 财政年份:
    2013
  • 资助金额:
    $ 71.16万
  • 项目类别:
The Impact of Health Homes in New York State on People with Substance Use Disorde
纽约州健康之家对药物滥用患者的影响
  • 批准号:
    8840210
  • 财政年份:
    2013
  • 资助金额:
    $ 71.16万
  • 项目类别:

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