Jonathan B. Bricker , Brianna M. Sullivan , Kristin E. Mull , Juan Lavista-Ferres , Margarita Santiago-Torres
{"title":"对话聊天机器人对戒烟的功效:QuitBot 全面随机对照试验方案。","authors":"Jonathan B. Bricker , Brianna M. Sullivan , Kristin E. Mull , Juan Lavista-Ferres , Margarita Santiago-Torres","doi":"10.1016/j.cct.2024.107727","DOIUrl":null,"url":null,"abstract":"<div><div>Globally, cigarette smoking results in over 8 million premature annual deaths. Addressing this issue requires high-impact, cost-effective population-level interventions for smoking cessation. Conversational chatbots offer a potential solution given the recent advancements in machine learning and large language models. Chatbots can deliver supportive, empathetic behaviors, personalized responses, and timely advice tailored to users' needs that is engaging through therapeutic conversations aimed at creating lasting social-emotional connections. Despite their promise, little is known about the efficacy and underlying mechanisms of chatbots for cigarette smoking cessation. We developed QuitBot, a quit smoking program of two to three-minute conversations covering topics ranging from motivations to quit, setting a quit date, choosing cessation medications, coping with triggers, maintaining abstinence, and recovering from a relapse. QuitBot employs conversational interactions, powered by an expert-curated large language model, allowing users to ask questions and receive personalized guidance on quitting smoking. Here, we report the design and execution of a randomized clinical trial comparing QuitBot (<em>n</em> = 760) against Smokefree TXT (SFT) text messaging program (n = 760), with a 12-month follow-up period. Both interventions include 42-days of content on motivations to quit, skills to cope with triggers, and relapse prevention. The key distinction between QuitBot and SFT is that QuitBot has communication and engagement features. This study aims to determine: whether QuitBot yields higher quit rates than SFT; and whether therapeutic alliance processes and engagement are mechanisms underlying cessation outcomes. Additionally, we will explore whether baseline factors including trust, social support, and demographics, moderate the efficacy of QuitBot.</div><div><strong>Trial Registration number</strong> <span><span>ClinicalTrials.gov</span><svg><path></path></svg></span> <span><span>NCT04308759</span><svg><path></path></svg></span></div></div>","PeriodicalId":10636,"journal":{"name":"Contemporary clinical trials","volume":"147 ","pages":"Article 107727"},"PeriodicalIF":2.0000,"publicationDate":"2024-10-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Efficacy of a conversational chatbot for cigarette smoking cessation: Protocol of the QuitBot full-scale randomized controlled trial\",\"authors\":\"Jonathan B. Bricker , Brianna M. Sullivan , Kristin E. Mull , Juan Lavista-Ferres , Margarita Santiago-Torres\",\"doi\":\"10.1016/j.cct.2024.107727\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<div><div>Globally, cigarette smoking results in over 8 million premature annual deaths. Addressing this issue requires high-impact, cost-effective population-level interventions for smoking cessation. Conversational chatbots offer a potential solution given the recent advancements in machine learning and large language models. Chatbots can deliver supportive, empathetic behaviors, personalized responses, and timely advice tailored to users' needs that is engaging through therapeutic conversations aimed at creating lasting social-emotional connections. Despite their promise, little is known about the efficacy and underlying mechanisms of chatbots for cigarette smoking cessation. We developed QuitBot, a quit smoking program of two to three-minute conversations covering topics ranging from motivations to quit, setting a quit date, choosing cessation medications, coping with triggers, maintaining abstinence, and recovering from a relapse. QuitBot employs conversational interactions, powered by an expert-curated large language model, allowing users to ask questions and receive personalized guidance on quitting smoking. Here, we report the design and execution of a randomized clinical trial comparing QuitBot (<em>n</em> = 760) against Smokefree TXT (SFT) text messaging program (n = 760), with a 12-month follow-up period. Both interventions include 42-days of content on motivations to quit, skills to cope with triggers, and relapse prevention. The key distinction between QuitBot and SFT is that QuitBot has communication and engagement features. This study aims to determine: whether QuitBot yields higher quit rates than SFT; and whether therapeutic alliance processes and engagement are mechanisms underlying cessation outcomes. Additionally, we will explore whether baseline factors including trust, social support, and demographics, moderate the efficacy of QuitBot.</div><div><strong>Trial Registration number</strong> <span><span>ClinicalTrials.gov</span><svg><path></path></svg></span> <span><span>NCT04308759</span><svg><path></path></svg></span></div></div>\",\"PeriodicalId\":10636,\"journal\":{\"name\":\"Contemporary clinical trials\",\"volume\":\"147 \",\"pages\":\"Article 107727\"},\"PeriodicalIF\":2.0000,\"publicationDate\":\"2024-10-28\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Contemporary clinical trials\",\"FirstCategoryId\":\"3\",\"ListUrlMain\":\"https://www.sciencedirect.com/science/article/pii/S1551714424003100\",\"RegionNum\":3,\"RegionCategory\":\"医学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q3\",\"JCRName\":\"MEDICINE, RESEARCH & EXPERIMENTAL\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Contemporary clinical trials","FirstCategoryId":"3","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S1551714424003100","RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q3","JCRName":"MEDICINE, RESEARCH & EXPERIMENTAL","Score":null,"Total":0}
Efficacy of a conversational chatbot for cigarette smoking cessation: Protocol of the QuitBot full-scale randomized controlled trial
Globally, cigarette smoking results in over 8 million premature annual deaths. Addressing this issue requires high-impact, cost-effective population-level interventions for smoking cessation. Conversational chatbots offer a potential solution given the recent advancements in machine learning and large language models. Chatbots can deliver supportive, empathetic behaviors, personalized responses, and timely advice tailored to users' needs that is engaging through therapeutic conversations aimed at creating lasting social-emotional connections. Despite their promise, little is known about the efficacy and underlying mechanisms of chatbots for cigarette smoking cessation. We developed QuitBot, a quit smoking program of two to three-minute conversations covering topics ranging from motivations to quit, setting a quit date, choosing cessation medications, coping with triggers, maintaining abstinence, and recovering from a relapse. QuitBot employs conversational interactions, powered by an expert-curated large language model, allowing users to ask questions and receive personalized guidance on quitting smoking. Here, we report the design and execution of a randomized clinical trial comparing QuitBot (n = 760) against Smokefree TXT (SFT) text messaging program (n = 760), with a 12-month follow-up period. Both interventions include 42-days of content on motivations to quit, skills to cope with triggers, and relapse prevention. The key distinction between QuitBot and SFT is that QuitBot has communication and engagement features. This study aims to determine: whether QuitBot yields higher quit rates than SFT; and whether therapeutic alliance processes and engagement are mechanisms underlying cessation outcomes. Additionally, we will explore whether baseline factors including trust, social support, and demographics, moderate the efficacy of QuitBot.
期刊介绍:
Contemporary Clinical Trials is an international peer reviewed journal that publishes manuscripts pertaining to all aspects of clinical trials, including, but not limited to, design, conduct, analysis, regulation and ethics. Manuscripts submitted should appeal to a readership drawn from disciplines including medicine, biostatistics, epidemiology, computer science, management science, behavioural science, pharmaceutical science, and bioethics. Full-length papers and short communications not exceeding 1,500 words, as well as systemic reviews of clinical trials and methodologies will be published. Perspectives/commentaries on current issues and the impact of clinical trials on the practice of medicine and health policy are also welcome.