Difficulty and Time Perceptions of Preparatory Activities for Quitting Smoking: Dataset

doi: 10.4121/5198f299-9c7a-40f8-8206-c18df93ee2a0.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: 10.4121/5198f299-9c7a-40f8-8206-c18df93ee2a0
Datacite citation style:
Albers, Nele; Mark A. Neerincx; Brinkman, Willem-Paul (2023): Difficulty and Time Perceptions of Preparatory Activities for Quitting Smoking: Dataset. Version 1. 4TU.ResearchData. dataset. https://doi.org/10.4121/5198f299-9c7a-40f8-8206-c18df93ee2a0.v1
Other citation styles (APA, Harvard, MLA, Vancouver, Chicago, IEEE) available at Datacite

This dataset contains the data on 144 daily smokers each rating 44 preparatory activities for quitting smoking (e.g., envisioning one's desired future self after quitting smoking, tracking one's smoking behavior, learning about progressive muscle relaxation) on their perceived ease/difficulty and required completion time. Since becoming more physically active can make it easier to quit smoking, some activities were also about becoming more physically active (e.g., tracking one's physical activity behavior, learning about what physical activity is recommended, envisioning one's desired future self after becoming more physically active). Moreover, participants provided a free-text response on what makes some activities more difficult than others.


The data was gathered during a study on the online crowdsourcing platform Prolific between 6 September and 16 November 2022. The Human Research Ethics Committee of Delft University of Technology granted ethical approval for the research (Letter of Approval number: 2338). 

In this study, daily smokers who were contemplating or preparing to quit smoking first filled in a prescreening questionnaire and were then invited to a repertory grid study if they passed the prescreening. In the repertory grid study, participants were asked to divide sets of 3 preparatory activities for quitting smoking into two subgroups. Afterward, they rated all preparatory activities on the perceived ease of doing them and the perceived required time to do them. Participants also provided a free-text response on what makes some activities more difficult than others.

The study was pre-registered in the Open Science Framework (OSF): https://osf.io/cax6f. This pre-registration describes the study setup, measures, etc. Note that this dataset contains only part of the collected data: the data related to studying the perceived difficulty of preparatory activities.

The file "Preparatory_Activity_Formulations.xlsx" contains the formulations of the 44 preparatory activities used in this study.


This dataset contains three types of data:

- Data from participants' Prolific profiles. This includes, for example, the age, gender, weekly exercise amount, and smoking frequency.

- Data from a prescreening questionnaire. This includes, for example, the stage of change for quitting smoking and whether people previously tried to quit smoking.

- Data from the repertory grid study. This includes the ratings of the 44 activities on ease and required time as well as the free-text responses on what makes some activities more difficult than others.

There is for each data file a file that explains each data column. For example, the file "prolific_profile_data_explanation.xlsx" contains the column explanations for the data gathered from participants' Prolific profiles.

Each data file contains a column called "rand_id" that can be used to link the data from the data files.

In the case of questions, please contact Nele Albers (n.albers@tudelft.nl) or Willem-Paul Brinkman (w.p.brinkman@tudelft.nl).

  • 2023-11-16 first online, published, posted
.py, .txt, .csv, .xlsx, .md, .pdf
  • 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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