An Administrative Supplement to Increase the Recruitment Rates of R01 DA047301 a Pivotal Trial of a Novel Digital Therapeutic for Smoking Cessation in Psychiatric Populations

提高 R01 DA047301 招募率的行政补充精神病人群戒烟新型数字疗法的关键试验

基本信息

批准号：
10818811
负责人：
Roger Vilardaga
金额：
$ 7.99万
依托单位：
DUKE UNIVERSITY
依托单位国家：
美国
项目类别：
财政年份：
2020
资助国家：
美国
起止时间：
2020-03-15 至 2024-12-31
项目状态：
已结题

项目摘要

PROJECT SUMMARY/ABSTRACT This Administrative Supplement to Support Existing NIH Grants and Cooperative Agreements (PA-20-272) will boost the recruitment capacity of R01 DA047301 to successfully meet the aims of an FDA pivotal trial. Tobacco use remains at alarming high rates amongst individuals with serious mental illness, such as schizophrenia spectrum, bipolar, and recurring depressive disorders. Smoking tobacco is the number one preventable cause of death in this population, shortening the lifespan of adults with serious mental illness by 25 years and contributing to $317 billion in healthcare expenditures and indirect loss of earnings and disability benefits. Novel and effective models to deliver wider-reaching smoking cessation interventions to individuals with serious mental illness are highly needed and indicated as a priority for NIH. Digital Therapeutics (DTx) are a promising method to deliver smoking cessation treatments to people with serious mental illness. Despite numerous DTx available to use for people with serious mental illness, our feasibility research has demonstrated that using DTx presents daunting challenges for this population. Based on this work, we developed Learn to Quit, a user-centered smoking cessation DTx tailored to the needs of individuals with serious mental illness. The DTx’s main active ingredient is a novel behavioral treatment that has shown promise as a smoking cessation intervention -- Acceptance and Commitment Therapy. The DTx provides education about the use of nicotine replacement therapy and recommendations from US Clinical Practice Guidelines to ensure alignment with best clinical practices. In a pilot randomized controlled trial (R00 DA037276), comparing Learn to Quit versus NCI’s QuitGuide, we demonstrated: (1) high recruitment and retention rates, (2) high levels of user engagement with Learn to Quit, (3) an effective method to conduct a large multi-site randomized controlled trial, and (4) promising cessation outcomes. While this developmental work provides initial evidence of Learn to Quit’s usability, feasibility, and preliminary efficacy, a large scale randomized controlled trial is needed to test its efficacy in a representative sample of patients with serious mental illness. Therefore, the proposed pivotal trial will: (1) employ a randomized parallel arm design to compare the efficacy of Learn to Quit vs. Brief Advice, (2) use rigorous methods to evaluate mechanistic processes of this DTx, and (3) examine the cost-effectiveness of Learn to Quit compared to a Brief Advice for this patient population. This pivotal trial addresses a serious problem -- high smoking rates among people with serious mental illness -- and proposes to test the efficacy of an easily-accessible smoking cessation intervention using technology with the potential for high population-level reach.

项目概要/摘要本支持现有 NIH 拨款和合作协议的行政补充文件 (PA-20-272) 将提高 R01 DA047301 的招募能力，以成功实现 FDA 关键试验的目标。患有严重精神疾病的人的烟草使用率仍然高得惊人，例如精神分裂症谱系、双相情感障碍和复发性抑郁症是吸烟的第一大原因。该人群中可预防的死亡原因，从而缩短患有严重精神疾病的成年人的寿命 25 年来，造成了 3170 亿美元的医疗保健支出以及间接收入和残疾损失为个人提供更广泛的戒烟干预措施的新颖有效的模型。患有严重精神疾病的患者非常需要，并被列为 NIH 的优先事项。这是一种为患有严重精神疾病的人提供戒烟治疗的有前景的方法。许多 DTx 可用于患有严重精神疾病的人，我们的可行性研究表明基于这项工作，我们证明使用 DTx 给这一人群带来了艰巨的挑战。开发了 Learn to Quit，这是一种以用户为中心的戒烟 DTx，适合患有烟瘾的个人的需求 DTx 的主要活性成分是一种已被证明的新型行为疗法。作为戒烟干预措施的承诺——DTx 提供的接受和承诺疗法。关于使用尼古丁替代疗法的教育和美国临床实践的建议确保与最佳临床实践保持一致的指南在一项试点随机对照试验中（R00）。 DA037276），比较“学习戒烟”与 NCI 的“戒烟指南”，我们证明：(1) 高招募率和保留率，(2) 学习戒烟的高水平用户参与度，(3) 进行戒烟的有效方法大型多中心随机对照试验，以及（4）有希望的戒烟结果。这项工作提供了“学习戒烟”的可用性、可行性和初步功效的初步证据，大规模需要随机对照试验来测试其在患有严重疾病的代表性样本中的疗效因此，拟议的关键试验将：（1）采用随机平行组设计来比较“学习戒烟”与“简短建议”的功效，(2) 使用严格的方法来评估机制 DTx 的流程，以及 (3) 检查“学习戒烟”与“简短建议”相比的成本效益这项关键试验解决了一个严重的问题——患有这种疾病的人的高吸烟率。严重的精神疾病——并建议测试一种容易实现的戒烟方法的功效使用具有高人口覆盖潜力的技术进行干预。