Collaboratively Setting Daily Step Goals with a Virtual Coach: Using Reinforcement Learning to Personalize Initial Proposals - Data and Analysis Code

doi: 10.4121/53f2d238-77fc-4045-89a9-fb7fa2871f1d.v1
The doi above is for this specific version of this dataset, which is currently the latest. Newer versions may be published in the future. For a link that will always point to the latest version, please use
doi: 10.4121/53f2d238-77fc-4045-89a9-fb7fa2871f1d
Datacite citation style:
Dierikx, Martin; Albers, Nele; Bouke L. Scheltinga; Brinkman, Willem-Paul (2024): Collaboratively Setting Daily Step Goals with a Virtual Coach: Using Reinforcement Learning to Personalize Initial Proposals - Data and Analysis Code. Version 1. 4TU.ResearchData. dataset.
Other citation styles (APA, Harvard, MLA, Vancouver, Chicago, IEEE) available at Datacite

This is the data and analysis code underlying the paper "Collaboratively Setting Daily Step Goals with a Virtual Coach: Using Reinforcement Learning to Personalize Initial Proposals" by Martin Dierikx, Nele Albers, Bouke L. Scheltinga, and Willem-Paul Brinkman. The paper develops a dialog to collaboratively set daily step goals with a virtual coach and analyzes the use of reinforcement learning to personalize the initial step goal proposal in the dialog.


The paper is based on data collected from a study conducted in June and July 2023 for the publicly available Master's thesis by Martin Dierikx ( In this study, 235 people were invited to between one and five conversational sessions with the text-based virtual coach Steph. In each session, Steph asked questions to determine people's current state based on their mood, sleep quality, available time, motivation, and self-efficacy. Afterward, Steph calculated a recommended daily step goal based on the user's previous walking behavior. Based on this recommended goal, Steph gave users three initial goal options, each 100 steps apart. Thereby, the options were randomly changed in one of five possible ways: 1) decrease by 400 steps, 2) decrease by 200 steps, 3) keep the same, 4) increase by 200 steps, or 5) increase by 400 steps. Users could select one of the presented goal options as well as indicate that they wanted a different goal. The next session started by asking users about the number of steps they took on the previous day. Data collected from this study was used to fit and analyze a reinforcement learning model for choosing initial step goal proposals.

The study was pre-registered in the Open Science Framework (OSF):

The Human Research Ethics Committee of Delft University of Technology approved our study (Letter of Approval number: 3016).

Links to further resources:

  1. The Rasa-based implementation of the virtual coach Steph is available here:
  2. A video of a dialog with the virtual coach is available here:


We collected data in several study components:

  1. Demographic data collected from participants' Prolific profiles (e.g., age, gender).
  2. Data collected from a prescreening questionnaire (e.g., Godin leisure-time physical activity).
  3. Data collected during the conversational sessions (e.g., mood, number of steps taken on the previous day).
  4. Data from the post-questionnaire (e.g., how personal the goals felt to participants, how difficult it was to reach the goals).

If you have any questions, please contact Nele Albers ( or Willem-Paul Brinkman (

  • 2024-01-23 first online, published, posted
.md, .pdf, .tar.gz, .ipynb, .Rmd, .csv, .bib, .html, .py
  • 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


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