%0 Generic
%A Albers, Nele
%A Melo, Francisco
%A Neerincx, Mark
%A Kudina, Olya
%A Brinkman, Willem-Paul
%D 2025
%T Psychological, economic, and ethical factors in human feedback for a chatbot-based smoking cessation intervention - Data and analysis code
%U 
%R 10.4121/c11b991b-0eda-4565-b7d0-6ca7fcd1cf7e.v1
%K adherence
%K artificial intelligence
%K behavior change
%K blended care
%K stepped care
%K chatbot
%K conversational agent
%K digital health
%K engagement
%K ethics
%X <p>This repository contains the data and analysis code for the paper "Psychological, economic, and ethical factors in human feedback for a chatbot-based smoking cessation intervention" by Nele Albers, Francisco S. Melo, Mark A. Neerincx, Olya Kudina, and Willem-Paul Brinkman.</p><p><br></p><p>The data and analysis code have previously been published in almost identical form as part of the PhD thesis by Nele Albers: https://doi.org/10.4121/1d9aa8eb-9e63-4bf5-98a3-f359dbc932a4. The main differences lie in the references to figures and tables due to different figure and table names.</p><p><br></p><p><strong>Study</strong></p><p>The paper is primarily based on data collected in a study conducted on the online crowdsourcing platform Prolific. In this study, daily smokers and vapers interacted with the text-based conversational agent Kai in up to five conversational sessions between 1 February and 19 March 2024. The Human Research Ethics Committee of Delft University of Technology granted ethical approval for the research (Letter of Approval number: 3683).</p><p><br></p><p>In each session, participants were assigned one of 37 preparatory activities for quitting smoking (e.g., envisioning their desired future self after quitting smoking/vaping, learning a breathing exercise, tracking their smoking behavior). Between each pair of sessions, participants had a 20% chance of receiving a feedback message from one of two human coaches, who were Master's students in Psychology. Out of 852 people who started the first conversational session, 500 completed all five sessions. 449 people further provided their preferences for allocating human feedback based on different principles in the post-questionnaire. There was also a follow-up questionnaire, but data from this questionnaire is not included in the analyses performed in this paper.</p><p><br></p><p>The study was pre-registered in OSF: https://doi.org/10.17605/OSF.IO/78CNR.</p><p><br></p><p>The implementation of the conversational agent Kai is available online: https://doi.org/10.5281/zenodo.11102861.</p><p><br></p><p>The 523 human feedback messages that were written can be found here: https://doi.org/10.4121/7e88ca88-50e9-4e8d-a049-6266315a2ece.</p><p><br></p><p><strong>Data</strong></p><p>This repository includes these types of anonymized data:</p><p><br></p><ol><li>Data from the prescreening questionnaire (e.g., stage of change for quitting smoking/vaping),</li><li>Data from people's Prolific profiles (e.g., age, gender),</li><li>Data from the conversational sessions with Kai (e.g., effort spent on activities),</li><li>Data from the post-questionnaire (e.g., preferences for allocation principles), and</li><li>Data on people clicking on the reading confirmation links in the human feedback messages.</li></ol><p><br></p><p>The variable "rand_id" is a random participant identifier and can be used to link data from different data files.</p><p><br></p><p><strong>Analysis code</strong></p><p>All our analyses are based on either R or Python. We provide code to allow them to be reproduced.</p><p><br></p><p><br></p><p>In the case of questions, please contact Nele Albers (n.albers@tudelft.nl) or Willem-Paul Brinkman (w.p.brinkman@tudelft.nl).</p>
%I 4TU.ResearchData