Thoracic Research Centre, Faculty of Medicine, The University of Queensland, Brisbane, Australia.
Australia e-Health Research Centre, Commonwealth Scientific and Industrial Research Organisation, Brisbane, Australia.
JMIR Hum Factors. 2024 Aug 19;11:e56505. doi: 10.2196/56505.
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.
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.
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.
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.
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.
我们的戒烟聊天机器人 Quin 是一个使用多阶段协同设计研究开发的原型，它通过智能手机应用程序为用户提供基于证据的个性化支持，帮助他们戒烟。在进行临床测试之前，我们的研究团队还进行了焦点小组评估，邀请利益相关者对 Quin 进行评估。
本研究旨在通过 beta 测试收集和比较戒烟者和戒烟专业人士（SCPs）对 Quin 原型的用户体验反馈，以指导正在进行的聊天机器人迭代和改进。
我们通过主动和被动招募的方式，在澳大利亚昆士兰州开展了面向 SCPs 和戒烟者的网络焦点小组。参与者在讨论前使用应用程序测试 1-2 周，并可以在应用程序中记录对话反馈。首先完成 SCP 焦点小组，以审查信息的广度和准确性，并在用户焦点小组之前使用敏捷流程优先处理和实施主要更新。我们使用内容分析对记录的应用程序反馈进行分类，并对焦点小组记录进行主题分析。
2022 年 8 月至 2023 年 6 月期间，共完成了 6 个焦点小组，其中 3 个是针对 SCPs（9 名参与者），3 个是针对戒烟者（7 名参与者）。4 名 SCPs 曾经吸烟，大多数戒烟者目前吸烟（5 名），2 名已经戒烟。焦点小组的平均时长为 58 分钟（SD 10.9；范围 46-74）。我们从焦点小组的反馈中确定了四个主要主题：（1）对话设计；（2）功能；（3）关系和拟人化；（4）作为戒烟支持工具的角色。根据 SCPs 的反馈，我们在两个队列之间对 Quin 进行了两项重大更新：（1）改进对话流程；（2）添加“危机时刻”对话树。参与者的反馈还为未来的戒烟聊天机器人发展提供了 17 项建议。
戒烟者和 SCPs 的反馈强调了聊天机器人功能的重要性，因为这是 Quin 对话设计和关系的基础。Quin 提供的准确戒烟信息和公正支持的便捷访问被认为是戒烟者的一个关键好处，这也有助于他们对聊天机器人产生责任感。研究结果将为正在进行的临床测试的成熟原型开发提供信息。
