Collaboratively Setting Daily Step Goals with a Virtual Coach: Using Reinforcement Learning to Personalize Initial Proposals - Data and Analysis Code

This is the data and analysis code underlying the paper "Collaboratively Setting Daily Step Goals with a Virtual Coach: Using Reinforcement Learning to Personalize Initial Proposals" by Martin Dierikx, Nele Albers, Bouke L. Scheltinga, and Willem-Paul Brinkman. The paper develops a dialog to collaboratively set daily step goals with a virtual coach and analyzes the use of reinforcement learning to personalize the initial step goal proposal in the dialog.<br><strong>Study</strong>The paper is based on data collected from a study conducted in June and July 2023 for the publicly available Master's thesis by Martin Dierikx (http://resolver.tudelft.nl/uuid:4f2c12de-9b9f-4e3f-ad3a-902947d693bb). In this study, 235 people were invited to between one and five conversational sessions with the text-based virtual coach Steph. In each session, Steph asked questions to determine people's current state based on their mood, sleep quality, available time, motivation, and self-efficacy. Afterward, Steph calculated a recommended daily step goal based on the user's previous walking behavior. Based on this recommended goal, Steph gave users three initial goal options, each 100 steps apart. Thereby, the options were randomly changed in one of five possible ways: 1) decrease by 400 steps, 2) decrease by 200 steps, 3) keep the same, 4) increase by 200 steps, or 5) increase by 400 steps. Users could select one of the presented goal options as well as indicate that they wanted a different goal. The next session started by asking users about the number of steps they took on the previous day. Data collected from this study was used to fit and analyze a reinforcement learning model for choosing initial step goal proposals.<br>The study was pre-registered in the Open Science Framework (OSF): https://doi.org/10.17605/OSF.IO/6JQPK.<br>The Human Research Ethics Committee of Delft University of Technology approved our study (Letter of Approval number: 3016).<br>Links to further resources:The Rasa-based implementation of the virtual coach Steph is available here: https://doi.org/10.5281/zenodo.8382413.A video of a dialog with the virtual coach is available here: https://youtu.be/FSpG-G0zc-o.<br><strong>Data</strong>We collected data in several study components:Demographic data collected from participants' Prolific profiles (e.g., age, gender).Data collected from a prescreening questionnaire (e.g., Godin leisure-time physical activity).Data collected during the conversational sessions (e.g., mood, number of steps taken on the previous day).Data from the post-questionnaire (e.g., how personal the goals felt to participants, how difficult it was to reach the goals).<br><br>If you have any questions, please contact Nele Albers ([e-mail]) or Willem-Paul Brinkman ([e-mail]).本数据集与分析代码为Martin Dierikx、Nele Albers、Bouke L. Scheltinga及Willem-Paul Brinkman的论文《与虚拟教练协作设定每日步数目标：利用强化学习（Reinforcement Learning）实现初始建议个性化》（原文标题：Collaboratively Setting Daily Step Goals with a Virtual Coach: Using Reinforcement Learning to Personalize Initial Proposals）提供了数据与分析代码支撑。该论文构建了一套与虚拟教练协作设定每日步数目标的对话交互流程，并分析了如何通过强化学习实现对话中初始步数目标建议的个性化定制。 <strong>研究</strong> 本研究基于2023年6月至7月开展的一项研究数据，该数据源自公开发布的Martin Dierikx硕士学位论文（http://resolver.tudelft.nl/uuid:4f2c12de-9b9f-4e3f-ad3a-902947d693bb）。本研究共邀请235名参与者参与1至5次与文本型虚拟教练Steph的对话会话。每次会话中，Steph将通过提问了解参与者的情绪、睡眠质量、可支配时间、运动动机及自我效能感，以此评估其当前状态。随后，Steph将基于用户既往步行行为计算推荐的每日步数目标，并据此为用户提供3个初始目标选项，各选项间相差100步。上述选项将通过以下5种预设方式之一随机调整：1）下调400步；2）下调200步；3）保持不变；4）上调200步；5）上调400步。参与者可选择任一展示的目标选项，也可自行提出自定义目标。下一次会话将首先询问参与者前一日的实际步行步数。本研究收集的数据被用于拟合并分析用于生成初始步数目标建议的强化学习模型。 本研究已在开放科学框架（Open Science Framework, OSF）进行预注册：https://doi.org/10.17605/OSF.IO/6JQPK。 本研究已获得代尔夫特理工大学人类研究伦理委员会批准（批准函编号：3016）。 更多资源链接： 基于Rasa的虚拟教练Steph实现代码：https://doi.org/10.5281/zenodo.8382413。 一段与虚拟教练的对话演示视频：https://youtu.be/FSpG-G0zc-o。 <strong>数据</strong> 本研究通过多个环节收集数据： 1. 从参与者的Prolific平台档案中获取的人口统计学数据（如年龄、性别）。 2. 预筛查问卷收集的数据（如Godin休闲体力活动量表（Godin leisure-time physical activity）得分）。 3. 对话会话过程中收集的数据（如情绪状态、前一日步行步数）。 4. 后测问卷收集的数据（如参与者对目标个性化程度的感知、达成目标的难度评价）。 如有任何疑问，请联系Nele Albers（邮箱：[e-mail]）或Willem-Paul Brinkman（邮箱：[e-mail]）。