Addressing people’s current and future states in a reinforcement learning algorithm for persuading to quit smoking and to be physically active: Data and analysis code

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Albers, Nele; Mark A. Neerincx; Brinkman, Willem-Paul (2022): Addressing people’s current and future states in a reinforcement learning algorithm for persuading to quit smoking and to be physically active: Data and analysis code. Version 2. 4TU.ResearchData. dataset.
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version 2 - 2022-11-11
version 1 - 2022-11-10

 This is the data and analysis code underlying the paper "Addressing people’s current and future states in a reinforcement learning algorithm for persuading to quit smoking and to be physically active" by Nele Albers, Mark A. Neerincx, and Willem-Paul Brinkman. This paper proposes a Reinforcement Learning (RL)-algorithm for persuading people in the context of a virtual coach for quitting smoking and becoming more physically active.


The paper is based on a longitudinal study on the 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 persuasion types to persuade people to do their activity. In the first two sessions, the persuasion type was chosen uniformly at random; in the last three sessions, the persuasion type was determined by a persuasion algorithm. 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): 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 effectiveness of the persuasion algorithm. 

Pointers to further resources:

  • Data on the acceptance of the virtual coach can be found here:
  • Data on users' needs for a digital smoking cessation application can be found here:
  • 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:
  • The implementation of the virtual coach Sam is available here:
  • The formulations for the 24 preparatory activities used in the study can be found in the supplementary material of the paper (S8 Appendix).


We collected four main types of data:

  • Perceived motivational impact and effort. The perceived motivational impact of the conversational sessions and the effort spent on the activities were used to evaluate the effectiveness of the persuasion algorithm. Both were measured during the conversational sessions.
  • Involvement in the activities. We used people's involvement in their activities for an exploratory subgroup analysis comparing the algorithm effectiveness for people with low and high involvement.
  • User characteristics (e.g., age, gender, Big-Five personality, quitter self-identity). This data was collected by means of questionnaires and from participants' Prolific profiles.
  • RL-samples (states, actions, rewards). This data was collected from the conversational sessions. The actions were the five persuasion types (e.g., consensus, action planning, no persuasion), and the reward was based on the effort.

Please consult the "Data"-folder for more information on the data we collected.

  • 2022-11-10 first online
  • 2022-11-11 published, posted
.zip .md .pdf .txt .py .csv .xlsx .RData .bib .Rmd .ipynb .png
  • 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).
TU Delft, Faculty of Electrical Engineering, Mathematics and Computer Science, Department of Intelligent Systems, Interactive Intelligence


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