Albers, Nele; Neerincx, M.A. (Mark); Brinkman, Willem-Paul (2023): Persuading to Prepare for Quitting Smoking with a Virtual Coach: Using States and User Characteristics to Predict Behavior - Data, Analysis Code and Appendix. Version 1. 4TU.ResearchData. dataset. https://doi.org/10.4121/22153898.v1

Dataset

x choose version: version 2 - 2023-06-09 (latest)

• Artificial Intelligence and Image Processing
• Public Health and Health Services

Behavior change support systems, Chatbot, Conversational agent, Demographics, eHealth, Persuasion algorithm, Physical activity, Reinforcement learning, Smoking, Virtual coach

20 May 2021 - 30 June 2021

CC BY 4.0

RefWorks, BibTeX, Reference Manager, Endnote, DataCite, NLM, DC, CFF

by Nele Albers , M.A. (Mark) Neerincx , Willem-Paul Brinkman

This repository contains the data, analysis code, and appendix of the paper "Persuading to Prepare for Quitting Smoking with a Virtual Coach: Using States and User Characteristics to Predict Behavior" by Nele Albers, Mark A. Neerincx, and Willem-Paul Brinkman, published in Proceedings of the 22nd International Conference on Autonomous Agents and Multiagent Systems (AAMAS 2023).

Data

The paper is based on data collected during a study on the online crowdsourcing platform Prolific run between 20 May 2021 and 30 June 2021. The Human Research Ethics Committee of Delft University of Technology granted ethical approval for the research (Letter of Approval number: 1523).

In this study, smokers who were contemplating or preparing to quit smoking interacted with the text-based virtual coach Sam in up to five conversational sessions. In each session, participants were assigned a new preparatory activity for quitting smoking, such as thinking of and writing down reasons for quitting smoking. Since becoming more physically active may make it easier to quit smoking, half of the activities addressed becoming more physically active. The virtual coach chose from five persuasive strategies to persuade people to do their activity. In the first two sessions, the persuasive strategy was chosen uniformly at random; in the last three sessions, the persuasive strategy was determined by a persuasion algorithm that differed between four conditions. In the next session, participants were asked to indicate the effort they spent on their activity, which served as basis for the reward signal for the persuasion algorithm. 

The study was pre-registered in the Open Science Framework (OSF): https://osf.io/k2uac. This pre-registration describes the study design, measures, etc. Note that the data we provide here is only a part of the data collected in the study, namely, the data related to studying the prediction of behavior (i.e., the effort people spent on their activities) based on user states and characteristics.

Analysis Code

Our analysis can be reproduced using Docker and Jupyter Notebook. We provide instructions for this in the README-files accompanying our analysis code.

Appendix

We also provide the Appendix of our paper, which contains more information on the virtual coach (including the conversation structure and preparatory activities), persuasion algorithm, data collection, optimal and worst policies computed for research questions Q3 and Q4, and the weighting of samples based on similarity for research question Q6.

Regarding the preparatory activities, note that there were two different formulations: one for during the session, and one for the reminder message people received on Prolific.The former asked people to do the activity "after this session" and told people that they would receive the video link in the Prolific reminder message in case the activity involved watching a video; the latter asked people to do the activity "before the next session" in sessions 1-4 and contained the video link in case the activity involved watching a video. All activity formulations can be found together with the virtual coach code: https://github.com/PerfectFit-project/virtual_coach_rl_persuasion_algorithm/blob/main/Activities.csv. Custom action code further modifies the reminder message activity formulation for session 5, which is the last session (https://github.com/PerfectFit-project/virtual_coach_rl_persuasion_algorithm/blob/main/actions/actions.py).

Further Resources

Here are some pointers to further resources:

• Data on the acceptance of the virtual coach can be found here: https://doi.org/10.4121/19934783.v1.
• Data on users' needs for a digital smoking cessation application can be found here: https://doi.org/10.4121/20284131.v2.
• Data on users' action plans for doing the activities (n = 469) and free-text responses to reflective questions about the activities (n = 2026) is available here: https://doi.org/10.4121/21905271.v1.
• The implementation of the virtual coach Sam is available here: https://doi.org/10.5281/zenodo.6319356. 
• Journal paper describing the persuasion algorithm and analyzing its effectiveness: https://doi.org/10.1371/journal.pone.0277295.

If you have questions about the data, analysis code, or appendix, please contact Nele Albers ([email protected]).

• 2023-02-24 first online, published, posted

4TU.ResearchData

.pdf .md .txt .py .xlsx .csv .ipynb .png .pickle .zip

• https://doi.org/10.5281/zenodo.6319356
• https://doi.org/10.17605/OSF.IO/K2UAC
• https://doi.org/10.4121/19934783.v1

• 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.

TU Delft, Faculty of Electrical Engineering, Mathematics and Computer Science, Department of Intelligent Systems, Interactive Intelligence