Feasibility of Using a Culturally Tailored Conversational Agent for promoting smoking cessation treatment utilization in African Americans who use cigarettes

项目总结/摘要 非裔美国人的发病率和死亡率高于白色美国人。一个促成因素是 健康差距是烟草使用以及获得和利用戒烟治疗的机会。及时 自适应干预使用预测模型来识别吸烟的高风险情况，并警告用户 使用应对技巧，如药物或行为策略。它们通常通过智能手机提供 技术.使用大型语言模型的人工智能技术的最新进展（例如，ChatGPT） 提供了一个机会，创造逼真和灵活的对话代理，以支持健康行为的改变， 那些医疗服务水平低的人。然而，在自动语音识别中存在种族差异， 可能会限制非裔美国人的效用。该中心拟议新项目的总体目标是 在明尼苏达州减少心血管疾病和促进公平的是制定一个及时 适应性干预触发管理的非裔美国人吸烟。为了实现这一目标，我们 有两个具体目标。(A1)开发一个新颖的和种族公正的戒烟个人助理 人工智能和大型语言模型（PASCAL）用于管理吸烟触发器。(A2)中试 PASCAL以确定其可行性和可接受性;并收集初步疗效数据。为支持这些 我们将进行三项研究。首先，我们将进行焦点小组（N=20-24），以确定 在吸烟的非裔美国人中戒烟的谈话媒介。接下来我们就 进行一项绿野仙踪研究，其中戒烟应用程序将使用以下人员之间的对话进行培训： 吸烟的非洲裔美国人（N=20）和一名烟草治疗专家冒充 会话代理。随着Just In Time Adaptive Intervention应用程序的开发，我们将进行 第三项研究是在吸烟的非洲裔美国人（N=100）中对该应用程序进行初步测试。所有参与者将获得一个 提供两周的尼古丁含片和自助戒烟材料。参与者将跟踪他们的吸烟情况， 戒烟前1周，以确定吸烟诱因。在本周末，参与者将被随机分配到 在PASCAL的支持下戒烟或自行戒烟。受试者将在术后8周接受随访 不干了共同主要结局是研究程序的可行性和PASCAL的可接受性。我们还将 收集关于锭剂使用、行为技能使用和CO验证的2和8点戒烟率的数据 周后辞职。这一建议意义重大，因为它是第一个测试使用大型语言模型的建议之一， 为健康行为改变的对话代理生成内容。此外，它以非洲为中心， 美国人，一个优先预防心血管疾病的人群，在发展一种新的健康 行为改变干预。

PROJECT SUMMARY/ABSTRACT African Americans have higher morbidity and mortality than do White Americans. One contributing factor to this health disparity is tobacco use and access to and utilization of smoking cessation treatment. Just in time adaptive interventions use predictive models to identify high risk situations for smoking and alert the user to use coping skills such as medication or behavioral strategies. They are typically delivered via smartphone technology. Recent advances in artificial intelligence technology using large language models (e.g., ChatGPT) provide an opportunity to create lifelike and flexible conversational agents to support health behavior change in those with low healthcare access. However, there are racial disparities in automated speech recognition that could limit utility among African Americans. The overarching goal of this proposed new project for the Center for Cardiovascular Disease Reduction and Equity Promotion Across Minnesota is to develop a Just In Time Adaptive Intervention for trigger management for African Americans who smoke. In support of this goal, we have two specific Aims. (A1) Develop a novel and racially unbiased Personal Assistant for Smoking Cessation with Artificial Intelligence and Large Language Models (PASCAL) for managing smoking triggers. (A2) Pilot test PASCAL to determine its feasibility and acceptability; and collect preliminary efficacy data. In support of these aims we will conduct 3 studies. First, we will conduct focus groups (N=20-24) to determine the acceptability of a conversational agent for smoking cessation among African Americans who smoke cigarettes. Next, we will conduct a Wizard of Oz study where the smoking cessation app will be trained using conversations between African American people who smoke (N=20) and a tobacco treatment specialist impersonating a conversational agent. Following the development of the Just In Time Adaptive Intervention app, we will conduct a third study to pilot test the App among African Americans who smoke (N=100). All participants will be given a two-week supply of nicotine lozenges and self-help cessation materials. Participants will track their smoking for 1 week pre-quit to identify smoking triggers. At the end of the week, participants will be randomly assigned to quit smoking with the support of PASCAL or to quit on their own. Participants will be followed for 8 weeks post quit. The co-primary outcomes are feasibility of study procedures and acceptability of PASCAL. We will also collect data on lozenge use, behavioral skills use, and CO-verified point prevalence abstinence at 2 and 8 weeks post quit. This proposal is significant as it is one of the first to test the use of large language models to generate content for conversational agents for health behavior change. In addition, it centers African Americans, a priority population for cardiovascular disease prevention in the development of a new health behavior change intervention.