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.
Study
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, daily 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): 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 effectiveness of the persuasion algorithm.
Pointers to further resources:
Data
We collected four main types of data:
Please consult the "Data"-folder for more information on the data we collected.
Changes in V3
Fixed an error in extract_RL_samples.py: For the computation of feature means used to make the state features binary, not all the last states of participants were considered. This has now been fixed. The change does not alter the results of the paper in any way because the old and new feature means are so similar that the resulting binary state features are exactly the same.
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