Co-Designing a Smoking Cessation Chatbot: Focus Group Study of End Users and Smoking Cessation Professionals.

IF 2.6 Q2 HEALTH CARE SCIENCES & SERVICES JMIR Human Factors Pub Date : 2024-08-19 DOI:10.2196/56505
Hollie Bendotti, Sheleigh Lawler, David Ireland, Coral Gartner, Henry M Marshall
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Abstract

Background: Our prototype smoking cessation chatbot, Quin, provides evidence-based, personalized support delivered via a smartphone app to help people quit smoking. We developed Quin using a multiphase program of co-design research, part of which included focus group evaluation of Quin among stakeholders prior to clinical testing.

Objective: This study aimed to gather and compare feedback on the user experience of the Quin prototype from end users and smoking cessation professionals (SCPs) via a beta testing process to inform ongoing chatbot iterations and refinements.

Methods: Following active and passive recruitment, we conducted web-based focus groups with SCPs and end users from Queensland, Australia. Participants tested the app for 1-2 weeks prior to focus group discussion and could also log conversation feedback within the app. Focus groups of SCPs were completed first to review the breadth and accuracy of information, and feedback was prioritized and implemented as major updates using Agile processes prior to end user focus groups. We categorized logged in-app feedback using content analysis and thematically analyzed focus group transcripts.

Results: In total, 6 focus groups were completed between August 2022 and June 2023; 3 for SCPs (n=9 participants) and 3 for end users (n=7 participants). Four SCPs had previously smoked, and most end users currently smoked cigarettes (n=5), and 2 had quit smoking. The mean duration of focus groups was 58 (SD 10.9; range 46-74) minutes. We identified four major themes from focus group feedback: (1) conversation design, (2) functionality, (3) relationality and anthropomorphism, and (4) role as a smoking cessation support tool. In response to SCPs' feedback, we made two major updates to Quin between cohorts: (1) improvements to conversation flow and (2) addition of the "Moments of Crisis" conversation tree. Participant feedback also informed 17 recommendations for future smoking cessation chatbot developments.

Conclusions: Feedback from end users and SCPs highlighted the importance of chatbot functionality, as this underpinned Quin's conversation design and relationality. The ready accessibility of accurate cessation information and impartial support that Quin provided was recognized as a key benefit for end users, the latter of which contributed to a feeling of accountability to the chatbot. Findings will inform the ongoing development of a mature prototype for clinical testing.

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共同设计戒烟聊天机器人:最终用户和戒烟专业人士的焦点小组研究。
背景:我们的戒烟聊天机器人原型Quin通过智能手机应用程序提供基于证据的个性化支持,帮助人们戒烟。我们通过多阶段共同设计研究计划开发了Quin,其中包括在临床测试前在利益相关者中对Quin进行焦点小组评估:本研究旨在通过 beta 测试流程收集和比较最终用户和戒烟专业人员(SCP)对 Quin 原型用户体验的反馈意见,为聊天机器人的持续迭代和改进提供参考:在主动和被动招募之后,我们与来自澳大利亚昆士兰州的戒烟专业人员和最终用户进行了基于网络的焦点小组讨论。参与者在焦点小组讨论前对应用程序进行了 1-2 周的测试,并可在应用程序中记录对话反馈。首先完成 SCP 的焦点小组讨论,以审查信息的广度和准确性,在最终用户焦点小组讨论之前,我们采用敏捷流程对反馈进行了优先排序,并将其作为主要更新内容加以实施。我们通过内容分析对记录在案的应用内反馈进行了分类,并对焦点小组记录进行了主题分析:在2022年8月至2023年6月期间,共完成了6个焦点小组;其中3个为SCP(人数=9),3个为最终用户(人数=7)。4名SCP曾吸烟,大多数最终用户目前吸烟(5人),2人已戒烟。焦点小组的平均持续时间为 58 分钟(标准差 10.9;范围 46-74)。我们从焦点小组的反馈中确定了四大主题:(1) 对话设计,(2) 功能性,(3) 关系性和拟人化,(4) 作为戒烟支持工具的作用。根据 SCP 的反馈意见,我们对 Quin 进行了两次重大更新:(1)改进了对话流程;(2)增加了 "危机时刻 "对话树。参与者的反馈还为未来戒烟聊天机器人的开发提供了 17 条建议:最终用户和 SCP 的反馈意见强调了聊天机器人功能的重要性,因为它是 Quin 对话设计和关联性的基础。Quin随时提供准确的戒烟信息和公正的支持被认为是终端用户的一个主要受益点,而后者有助于增强对聊天机器人的责任感。研究结果将为正在进行的成熟原型开发提供参考,以便进行临床测试。
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来源期刊
JMIR Human Factors
JMIR Human Factors Medicine-Health Informatics
CiteScore
3.40
自引率
3.70%
发文量
123
审稿时长
12 weeks
期刊最新文献
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