Dataset and Analyses for Using a conversational agent for thought recording as a cognitive therapy task: feasibility, content, and feedback

doi: 10.4121/20137736.v2
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: 10.4121/20137736
Datacite citation style:
Franziska Burger (2022): Dataset and Analyses for Using a conversational agent for thought recording as a cognitive therapy task: feasibility, content, and feedback. Version 2. 4TU.ResearchData. dataset. https://doi.org/10.4121/20137736.v2
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Dataset
choose version:
version 2 - 2022-09-05 (latest)
version 1 - 2022-07-07

This dataset contains all data and analysis scripts pertaining to the research conducted for the frontiers paper: "Using a conversational agent for thought recording as a cognitive therapy task: feasibility, content, and feedback." Following a literature review that we conducted in 2017 and 2018 on the technological state of the art of e-mental health for depression, we saw an opportunity to use technology in a more dialogical way than was being done to date.

We therefore developed a conversational agent to support people in regularly recording their thoughts. This thought recording is a common technique in cognitive therapy. The cognitive approach to psychotherapy aims to change patients' maladaptive schemas, that is, overly negative views on themselves, the world, or the future. To obtain awareness of these views, they record their thought processes in situations that caused pathogenic emotional responses. We recruited 308 participants through Prolific, a crowd-sourcing platform for research participants. The participants interacted with our chatbot in two sessions, one practice session of two thought records based on scenarios and one actual session in which we asked to complete at least one personal thought record but as many additional ones as they wanted. We assessed the feasibility of completing the task with the agent, the content of the personal thought records, and whether the agent providing feedback on the content of

the thought record (using natural language processing) had a positive e ect on the number of voluntarily completed thought records and participant's engagement in self-reection. We here deliver:

  1. a natural language dataset: the thoughts delineated by participants in the scenario-based and open thought records
  2. the coding of all personal thought records on their content by two independent coders: all thought records of the second session were labeled with respect to their content on the DIAMONDS and on three additional categories (COVID, Achievement/Competence, and Comprehensibility)
  3. analyses to test the hypotheses related to whether the feedback of the agent can increasemotivation to complete thought records
  4. additional materials (scenarios, qualtrics surveys, data management plan) that could assist in the replication of the study.
history
  • 2022-07-07 first online
  • 2022-09-05 published, posted
publisher
4TU.ResearchData
format
.csv, .txt, .pdf, .png, .R, .Rnw, .cls, .bib, .sty, .bst, .zip
funding
  • 4TU Humans & Technology Smart Social Systems and Spaces (S4) for Living Well
  • 4TU Humans & Technology Pride and Prejudice
organizations
TU Delft, Faculty of Electrical Engineering, Mathematics and Computer Science, Department of Intelligent Systems
4TU research center for Humans and Technology

DATA

files (1)