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

This repository contains the data, analysis code, and appendix for the paper "Reinforcement learning for proposing smoking cessation activities that build competencies: Combining two worldviews in a virtual coach" by Nele Albers, Mark A. Neerincx, and Willem-Paul Brinkman. <br>The data and large parts of the analysis code have previously been published in the repository for the PhD thesis chapter by Nele Albers that can be found via this DOI: https://doi.org/10.4121/9c4d9c35-3330-4536-ab8d-d5bb237c277d. The data is unchanged, but some additional analyses have been added (e.g., reporting the characteristics of the participants of the repertory grid study with smokers, repeating the analysis for AQ1 for different discount factors) in this repository compared to the one for the PhD thesis chapter.<br><strong>Study</strong>The paper 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 between September and October 2022. 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 five conversational 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 paper can be found here: https://doi.org/10.4121/5198f299-9c7a-40f8-8206-c18df93ee2a0. To reproduce our reporting of the characteristics of the participants of this study, you will also need to download data from this other repository. More information on this is provided in Readme-files in this repository.-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 paper'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]).

本仓库收录了论文《用于提出构建能力的戒烟活动的强化学习：在虚拟教练中融合两种世界观》（原文标题：Reinforcement learning for proposing smoking cessation activities that build competencies: Combining two worldviews in a virtual coach）的相关数据、分析代码及附录，该论文作者为Nele Albers、Mark A. Neerincx与Willem-Paul Brinkman。
本仓库中的数据与大部分分析代码，此前已发布于Nele Albers博士论文章节对应的仓库，该仓库可通过以下DOI获取：https://doi.org/10.4121/9c4d9c35-3330-4536-ab8d-d5bb237c277d。相较于博士论文章节的仓库，本仓库的数据未作任何改动，但新增了部分分析内容，例如报告吸烟者参与构念网格（repertory grid）研究的参与者特征、针对不同折扣因子重复AQ1的分析。
<strong>研究</strong>
本论文基于三项研究收集的数据展开。
研究1
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该研究授予伦理批准（批准函编号：2338）。
本研究中，处于戒烟思考或准备阶段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网格（repertory grid）研究。在该研究中，参与者需将3项戒烟准备活动的集合划分为两个子组，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感知所需耗时进行评分。该研究的数据可通过以下DOI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）中预注册：https://osf.io/cax6f。
研究2
本研究于2022年9月至10月期间针对戒烟专家开展了第二项构念网格（repertory grid）研究。研究要求这些戒烟专家根据问题“从吸烟者通过活动所构建的戒烟能力维度来看，两项活动在某些方面存在相似性，但与第三项活动存在差异”，将3项戒烟准备活动的集合划分为两个子组。
本研究与吸烟者构念网格研究一同在OSF中预注册：https://osf.io/cax6f，且适用相同的伦理批准。
研究3
本研究同样在在线众包平台Prolific上开展。在2023年7月21日至8月27日期间，每日吸烟者可与对话代理（conversational agent）Mel进行最多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日为该研究授予伦理批准（批准函编号：2939）。
在每一轮会话中，参与者将被分配一项戒烟活动：44项准备活动之一，或9项说服性活动之一。共有682人开启了首轮会话，349人完成了第5轮会话。
本研究已在OSF中预注册：https://doi.org/10.17605/OSF.IO/NUY4W。
对话代理Mel的实现代码可通过以下DOI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的数据（例如吸烟者提供的子组标签与活动评分）。本论文未使用的研究1额外数据可通过以下DOI获取：https://doi.org/10.4121/5198f299-9c7a-40f8-8206-c18df93ee2a0。若要复现本研究对该研究参与者特征的报告内容，还需从该仓库下载额外数据，相关更多说明可参见本仓库的README文件。
- 研究2的数据（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程度）。
- 研究3的数据：
参与者Prolific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）
与Mel的对话会话数据（例如活动投入精力情况）
后测问卷数据（例如吸烟频率、戒烟自我认同）
随访问卷数据（例如吸烟频率、戒烟自我认同、每周锻炼量）
<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信息。</em>
<strong>分析代码</strong>
所有分析均基于R或Python语言实现，本仓库提供可复现全部分析流程的代码。
<strong>附录</strong>
本仓库还提供论文附录，其中包含44项准备活动与9项说服性活动的具体表述等内容。

如有任何疑问，请联系Nele Albers（邮箱：[e-mail]）或Willem-Paul Brinkman（邮箱：[e-mail]）。