The impact of human feedback in a chatbot-based smoking cessation intervention: An empirical study into psychological, economic, and ethical factors - Data and analysis code for the PhD thesis chapter
doi: 10.4121/1d9aa8eb-9e63-4bf5-98a3-f359dbc932a4
CC BY 4.0
This repository contains the data and analysis code for the chapter "The impact of human feedback in a chatbot-based smoking cessation intervention: An empirical study into psychological, economic, and ethical factors" from the PhD thesis by Nele Albers.
Study
The chapter 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).
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 chapter.
The study was pre-registered in OSF: https://doi.org/10.17605/OSF.IO/78CNR.
The implementation of the conversational agent Kai is available online: https://doi.org/10.5281/zenodo.11102861.
The 523 human feedback messages that were written can be found here: https://doi.org/10.4121/7e88ca88-50e9-4e8d-a049-6266315a2ece.
Data
This repository includes these types of anonymized data:
- Data from the prescreening questionnaire (e.g., stage of change for quitting smoking),
- Data from people's Prolific profiles (e.g., age, gender),
- Data from the conversational sessions with Kai (e.g., effort spent on activities),
- Data from the post-questionnaire (e.g., preferences for allocation principles), and
- Data on people clicking on the reading confirmation links in the human feedback messages.
The variable "rand_id" is a random participant identifier and can be used to link data from different data files.
Analysis code
All our analyses are based on either R or Python. We provide code to allow them to be reproduced.
In the case of questions, please contact Nele Albers (n.albers@tudelft.nl) or Willem-Paul Brinkman (w.p.brinkman@tudelft.nl).
- 2025-01-08 first online, published, posted
- This work is part of the multidisciplinary research project Perfect Fit, which is supported by several funders organized by the Netherlands Organization for Scientific Research (NWO), program Commit2Data - Big Data & Health (project number 628.011.211). Besides NWO, the funders include the Netherlands Organisation for Health Research and Development (ZonMw), Hartstichting, the Ministry of Health, Welfare and Sport (VWS), Health Holland, and the Netherlands eScience Center.
DATA - under embargo
The files in this dataset are under embargo until 2025-02-25.
Reason
The PhD thesis has not yet been published.
