Full Scale Randomized Trial of an Innovative Conversational Agent for Smoking Cessation

项目摘要/摘要(说明) 吸烟每年造成48万人过早死亡和三分之一的癌症死亡。 我们。对高影响、高成本效益的人群层面的吸烟干预措施的巨大需求 停止。在过去的15年里，移动电话传递的短信干预措施，如NCI的 SmokeFree TXT已经成为解决这一需求的一项突出技术。然而，很像所有广泛的 可用于戒烟的技术(例如，网站)、短信干预措施已适度戒烟 利率，很大程度上是由低参与度推动的。幸运的是，一项新技术提供了一种治疗对话 解决影响文本消息传递和其他当前数字技术的参与度问题 戒烟。机器学习、自然语言处理和云计算的进步现在 使创建和广泛传播计算机支持的对话代理(CA)成为可能 旨在通过对话与用户建立长期社交情感联系的数字教练。 CA是支持性的、有同情心的、能反思的倾听、提供个性化的回应、并提供目标设定和 建议的时机与用户的需求相适应。关于戒烟的CA，主要的 知识差距是：(1)其有效性，(2)理论机制，(3)成本效益。也是 未被研究的是潜在的用于戒烟的CA的基线调节剂。我们最近开发了一个CA 用于戒烟的QuitBot，在一项日记研究中对其进行了评估，然后在试点随机试验中进行了测试 对照试验(N=306)，与NCI的无烟血栓试验进行比较。试验性随机对照试验设计是非常可行的 93%的患者获得三个月的随访率。QuitBot拥有：(A)高参与度和(B)高离职率 三个月的随访--与SmokeFree TXT相比非常有希望。解决这些知识差距 在我们QuitBot研究成果的基础上，该项目将进行随机对照 QuitBot(n=760)与SmokeFree TXT(n=760)的试验，随访12个月以确定 QuitBot：(1)戒烟率是否高于SmokeFree TXT，(2)是否有戒烟结果 显著受治疗联盟流程和参与度的影响，以及(3)与 免烟TXT。此外，这项研究将探索这些基线因素是否会影响有效性 QuitBot：信任、社会支持和人口统计(例如，性别)。这一创新项目将推进以下领域 对戒烟和改变健康行为的CA的研究-- 无论结果是肯定的还是空的。积极的结果可能会对人口水平产生很大影响 并鼓励对CA的传播和实施以及CA的改编以 吸烟者、语言、社区和医疗环境的多个亚群。

PROJECT SUMMARY/ABSTRACT (DESCRIPTION) Cigarette smoking accounts for 480,000 premature deaths and one third of all cancer deaths annually in the US. There is enormous need for high-impact, cost-effective population-level interventions for smoking cessation. For the past 15 years, mobile phone-delivered text messaging interventions such as the NCI’s SmokefreeTXT have been a prominent technology addressing this need. However, very much like all widely available technologies for smoking cessation (e.g., websites), text messaging interventions have modest quit rates, driven largely by low engagement. Fortunately, a new technology provides a therapeutic conversation to address the problem of engagement that impacts text messaging and other current digital technologies for smoking cessation. Advances in machine learning, natural language processing, and cloud computing are now making it possible to create and widely disseminate conversational agents (CAs), which are computer-powered digital coaches designed to form long-term social-emotional connections with users through conversations. CAs are supportive, empathic, reflectively listen, provide personalized responses, and offer goal setting and advice appropriately timed to the needs of the user. Regarding CAs for smoking cessation, the major knowledge gaps are: (1) their efficacy, (2) theoretical mechanisms, and (3) the cost-effectiveness. Also unexplored are the potential baseline moderators of CAs for smoking cessation. We recently developed a CA for smoking cessation, called “QuitBot,” evaluated it in a diary study, and then tested it in a pilot randomized controlled trial (N = 306), comparing it with the NCI’s SmokefreeTXT. The pilot RCT design was very feasible with 93% three-month follow-up. QuitBot had: (a) high participant engagement and (b) high quit rates at the three-month follow-up—very promising in comparison with SmokefreeTXT. Addressing these knowledge gaps and building on the promising results of our QuitBot research, the project will conduct a randomized controlled trial of QuitBot (n = 760) versus SmokefreeTXT (n = 760) with 12-month follow-up in order to determine whether QuitBot: (1) provides higher quit rates than SmokefreeTXT, (2) has smoking cessation outcomes significantly mediated by therapeutic alliance processes and engagement, and (3) is cost-effective vs. SmokefreeTXT. In addition, this study will explore whether these baseline factors moderate the effectiveness of QuitBot: trust, social support, and demographics (e.g., sex). This innovative project will advance the fields of research on CAs both for smoking cessation in particular and for health behavior change in general— regardless of whether the results are positive or null. Positive results could have high population-level impact and stimulate new lines of research into CA dissemination and implementation, and the adaptation of CAs for multiple subpopulations of smokers, languages, and community and medical settings.