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Preparing for Quitting Smoking and Becoming More Physically Active with a Virtual Coach: Reflections for Persuasive Messages and Action Plans

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posted on 2023-01-17, 14:16 authored by Nele AlbersNele Albers, M.A. (Mark) Neerincx, Willem-Paul BrinkmanWillem-Paul Brinkman

This dataset contains action plans for doing preparatory activities (n = 469) and free-text responses to reflective questions about preparatory activities (n = 2026) in the context of quitting smoking and becoming more physically active with a virtual coach. 289 reflections concern the views of experts, 750 the views of similar people, and 987 commitment to one's decision to quit smoking. The dataset also contains data on user characteristics (e.g., age, personality).

Study

The data was gathered during a longitudinal study on the online crowdsourcing platform Prolific 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, 671 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 can make it easier to quit smoking, half of the activities addressed preparing for becoming more physically active. Sam persuaded people to do their assigned activity using one of five persuasion types (commitment, consensus, authority, action planning, and no persuasion). For the persuasion types of commitment, consensus, and authority, Sam first uttered a persuasive message, followed by a reflective question that participants were asked to provide a free-text response to (e.g. "Please tell me what you think: In what way does doing this activity match your decision to successfully quit smoking?"). For the persuasion type of action planning, participants were asked to type an action plan for doing the activity into the chat. After the five sessions, participants filled in a post-questionnaire in which they were asked about their ease of and motivation to do their preparatory activities via two items each.

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 this dataset contains only part of the collected data, namely, the data related to studying the  reflections and action plans created by participants. Other data from this study has been published in separate datasets:

Since the same random participant identifiers are used in these datasets, data from the separate datases can be linked.

Pointers to more information on the study:

Data

This dataset contains five types of data (explained in the file "_Explanation_of_Data_Files.xlsx"):

  • Data from participants' Prolific profiles. This includes data on demographics (e.g., age range, household size, household income) as well as smoking and physical activity behavior (e.g., weekly exercise amount, smoking frequency).
  • Data from a pre-screening questionnaire. This includes, for example, the responses to informed consent questions.
  • Data from a pre-questionnaire. This includes data on smoking and physical activity behavior, as well as personality and need for cognition.
  • Data from the conversational sessions. This includes the action plans and reflective question answers, the effort people spent on their activity from the previous session, people's mood (e.g., "happy", "miserable", "gloomy"), answers to state questions (e.g., having sufficient time to do the assigned activity), and the persuasion type used by the virtual coach.
  • Data from a post-questionnaire. This includes data on the ease of and motivation to do the preparatory activities.

There is a separate data file for each type of data. For each data file, there is also a corresponding .xlsx-file explaining each measure in detail.

In case of questions about this dataset, please contact Nele Albers (n.albers@tudelft.nl).

Funding

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.

History

Publisher

4TU.ResearchData

Time coverage

20 May 2021 - 30 June 2021

Format

.xlsx .csv

Organizations

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