Reinforcement learning for proposing smoking cessation activities that build competencies: Combining two worldviews in a virtual coach - Data, analysis code, and appendix for the PhD thesis chapter

This repository contains the data, analysis code, and appendix for the chapter "Reinforcement learning for proposing smoking cessation activities that build competencies: Combining two worldviews in a virtual coach" from the PhD thesis by Nele Albers.<br><strong>Study</strong>The chapter is based on data collected in three studies.<br>Study 1We conducted this study on the online crowdsourcing platform Prolific between 6 September and 16 November 2022. The Human Research Ethics Committee of Delft University of Technology granted ethical approval for the research (Letter of Approval number: 2338). <br>In this study, daily smokers who were contemplating or preparing to quit smoking first filled in a prescreening questionnaire and were then invited to a repertory grid study if they passed the prescreening. In the repertory grid study, participants were asked to divide sets of 3 preparatory activities for quitting smoking into two subgroups. Afterward, they rated all preparatory activities on the labels given to the subgroups.<br>Participants also rated all preparatory activities on the perceived ease of doing them and the perceived required time to do them. This data can be found in this repository: https://doi.org/10.4121/5198f299-9c7a-40f8-8206-c18df93ee2a0.<br>The study was pre-registered in the Open Science Framework (OSF): https://osf.io/cax6f.<br>Study 2We performed a second repertory grid study with smoking cessation experts. These smoking cessation experts were also asked to divide sets of 3 preparatory activities for quitting smoking into two subgroups based on the question “When it comes to competencies for quitting smoking that smokers build by doing the activities, how are two activities alike in some way but different from the third activity?”<br>The study was pre-registered in OSF together with the repertory grid study with smokers: https://osf.io/cax6f. The same ethical approval also applies.<br>Study 3We conducted a third study on the online crowdsourcing platform Prolific. In this study, daily smokers interacted with the conversational agent Mel in up to fiveconversational sessions between 21 July and 27 August 2023. The Human Research Ethics Committee of Delft University of Technology granted ethical approval for the research (Letter of Approval number: 2939) on 31 March 2023.<br>In each session, participants were assigned a new activity for quitting smoking: one of 44 preparatory activities or one of 9 persuasive activities. 682 people started the first session and 349 people completed session 5.<br>The study was pre-registered in OSF: https://doi.org/10.17605/OSF.IO/NUY4W.<br>The implementation of the conversational agent Mel is available online: https://doi.org/10.5281/zenodo.8302492.<br><strong>Data</strong>We provide data on the 3 studies:-Data on study 1 (e.g., the subgroup labels and activity ratings provided by smokers). Additional data from study 1 not used in this chapter can be found here: https://doi.org/10.4121/5198f299-9c7a-40f8-8206-c18df93ee2a0.-Data on study 2 (e.g., the subgroup labels and activity ratings provided by experts, as well as the self-reported expertise of the experts)-Data on study 3:Data from participants' Prolific profiles (e.g., age, gender)Data from the prescreening questionnaire (e.g., smoking frequency, quitter self-identity)Data from the conversational sessions with Mel (e.g., effort spent on activities)Data from the post-questionnaire (e.g., smoking frequency, quitter self-identity)Data from the follow-up questionnaire (e.g., smoking frequency, quitter self-identity, weekly exercise amount)<em>The variable "rand_id" is a random participant identifier and can be used to link data from different data files.</em><br><strong>Analysis code</strong>All our analyses are based on either R or Python. We provide code to allow them to be reproduced.<br><strong>Appendix</strong>We also provide the chapter's appendix, which includes, for example, the formulations of the 44 preparatory and 9 persuasive activities.<br><br>In the case of questions, please contact Nele Albers ([e-mail]) or Willem-Paul Brinkman ([e-mail]).

本仓库包含Nele Albers博士论文章节「强化学习用于提出构建戒烟能力的戒烟活动：在虚拟教练中融合两种世界观」的配套数据、分析代码与附录。
<strong>研究</strong>
本章节基于三项研究收集的数据开展。
<strong>研究1</strong>
本研究于2022年9月6日至11月16日期间在在线众包平台Prolific上开展。代尔夫特理工大学人类研究伦理委员会为本次研究授予伦理批准（批准函编号：2338）。
本研究中，处于戒烟思考期或准备期的每日吸烟者首先需填写预筛选问卷，通过预筛选的参与者将受邀参与构念方格法（repertory grid）研究。在该研究中，参与者需将3项戒烟准备活动划分为两个子组，随后根据为子组设定的标签对所有准备活动进行评分。
参与者还需对所有准备活动的感知执行难度与所需耗时进行评分。本研究相关数据可于本仓库中获取：https://doi.org/10.4121/5198f299-9c7a-40f8-8206-c18df93ee2a0。
本研究已在开放科学框架（Open Science Framework, OSF）上预先注册：https://osf.io/cax6f。
<strong>研究2</strong>
我们针对戒烟领域专家开展了第二项构念方格法研究。研究要求专家基于以下问题将3项戒烟准备活动划分为两个子组：「就吸烟者通过开展活动所构建的戒烟能力而言，两项活动在哪些方面具有相似性，同时又与第三项活动存在差异？」
本研究与针对吸烟者的构念方格法研究一同在OSF上预先注册：https://osf.io/cax6f，且适用相同的伦理批准要求。
<strong>研究3</strong>
我们在在线众包平台Prolific上开展了第三项研究。2023年7月21日至8月27日期间，每日吸烟者可与对话智能体（conversational agent）Mel进行至多5轮对话会话。代尔夫特理工大学人类研究伦理委员会于2023年3月31日为本次研究授予伦理批准（批准函编号：2939）。
每轮会话中，参与者将被分配一项全新的戒烟活动：44项戒烟准备活动或9项劝服性活动中的一项。共有682名参与者开启了首轮会话，349名参与者完成了第5轮会话。
本研究已在OSF上预先注册：https://doi.org/10.17605/OSF.IO/NUY4W。
对话智能体Mel的实现代码可在线获取：https://doi.org/10.5281/zenodo.8302492。
<strong>数据</strong>
本仓库提供三项研究的相关数据：
- 研究1数据：例如吸烟者提供的子组标签与活动评分。本章未使用的研究1额外数据可于以下链接获取：https://doi.org/10.4121/5198f299-9c7a-40f8-8206-c18df93ee2a0。
- 研究2数据：例如专家提供的子组标签与活动评分，以及专家自我报告的专业水平。
- 研究3数据：
- 参与者Prolific平台档案数据（例如年龄、性别）
- 预筛选问卷数据（例如吸烟频率、戒烟自我认同度）
- 与Mel的对话会话数据（例如活动投入精力）
- 事后问卷数据（例如吸烟频率、戒烟自我认同度）
- 追踪问卷数据（例如吸烟频率、戒烟自我认同度、周运动时长）
<em>变量「rand_id」为随机参与者标识符，可用于关联不同数据文件中的相关数据。</em>
<strong>分析代码</strong>
本研究的所有分析均基于R语言或Python语言，我们提供了可复现分析过程的代码。
<strong>附录</strong>
本仓库同时提供本章的附录内容，例如44项戒烟准备活动与9项劝服性活动的具体表述。
如有任何疑问，请联系Nele Albers（邮箱：[e-mail]）或Willem-Paul Brinkman（邮箱：[e-mail]）。