1. ABOUT THE DATASET -------------------- Title: Dataset for a randomised factorial experiment to optimise an information leaflet for women with breast cancer Creator(s): Sophie M. C. Green[1], Samuel G. Smith[1] Organisation(s): 1. Leeds Institute of Health Sciences, University of Leeds, UK Rights-holder(s):Unless otherwise stated, Copyright 2023 University of Leeds Publication Year: 2023 Description: Most women with breast cancer are prescribed adjuvant endocrine therapy (AET) to prevent recurrence and mortality, but adherence is low. Negative beliefs about the necessity of AET and high concerns are barriers to adherence. We aimed to optimize the content of an information leaflet intervention, to support AET beliefs. We conducted an online screening experiment using a 2^5 factorial design to optimize the leaflet. The leaflet had five candidate components, each operationalised as factors with two levels; 1) diagrams about AET mechanisms (on/off); 2) infographics displaying AET benefits (enhanced/basic); 3) AET side-effects (enhanced/basic); 4) answers to AET concerns (on/off); 5) breast cancer survivor (patient) input: quotes and photographs (on/off). Healthy adult women (n=1604), recruited via a market research company, were randomized to one of 32 experimental conditions, which determined the combination of components received. Participants completed the beliefs about medicines questionnaire before and after viewing the leaflet. After viewing the leaflet, participants also completed a modified version of the satisfaction with information about medicines questionnaire to assess their satisfaction with information received about AET, and eight true/false questions about AET, to assess their knowledge about AET after reading the leaflet. Cite as: Sophie M. C. Green, Samuel G. Smith. Dataset for a randomised factorial experiment to optimise an information leaflet for women with breast cancer. University of Leeds. [Dataset]. https://doi.org/10.5518/1467 Related publication: Sophie M. C. Green, Louise H. Hall, David P. French, Nikki Rousseau, Catherine Parbutt, Rebecca Walwyn, Samuel G. Smith, on behalf of the ROSETA investigators. Optimization of an information leaflet to influence medication beliefs in women with breast cancer: a randomized factorial experiment. Annals of Behavioral Medicine. 57(11), 988-1000. https://doi.org/10.1093/abm/kaad037 Sophie M. C. Green, Samuel G. Smith, Linda M. Collins, Jillian C. Strayhorn. Decision-making in the multiphase optimization strategy (MOST): Applying decision analysis for intervention value efficiency (DAIVE) to optimize an information leaflet to promote key antecedents of medication adherence. Translational Behavioral Medicine. Contact: Sophie M. C. Green. Leeds Institute of Health Science, University of Leeds, Clarendon Way, Leeds, LS2 9NL, UK. Email: S.m.c.green@leeds.ac.uk 2. TERMS OF USE --------------- This dataset is licensed under a Creative Commons Attribution 4.0 International Licence: https://creativecommons.org/licenses/by/4.0/. 3. PROJECT AND FUNDING INFORMATION ---------------------------------- Title: Refining and Optimising a behavioural intervention to support endocrine therapy adherence (ROSETA) Dates: 2020-2026 Funding organisation: NIHR Grant no.: This report is independent research supported by the National Institute for Health Research NIHR Advanced Fellowship, Dr Samuel Smith NIHR300588. Smith also acknowledges funding support from a Yorkshire Cancer Research University Academic Fellowship. SG acknowledges receipt of a Health and Behavior International Collaborative Research Award, sponsored by the International Behavioral Trials Network. The views expressed in this publication are those of the author(s) and not necessarily those of the NHS, the National Institute for Health Research or the Department of Health and Social Care. The funders had no role in the design of the study, data collection, analysis, interpretation of data, and in the writing of this manuscript. 4. CONTENTS ----------- File listing Folder: Raw_Data File: IL_Optimisation_Data_Primary_and_Secondary.csv - CSV file containing the raw dataset without the R scripts applied to it. Folder: Raw_Data File: IL_Data_Cleaned_Primary_and_Secondary.csv- CSV file containing the cleaned dataset after the data cleaning R script has been applied to the raw dataset. Folder: R_Scripts File: IL_Optimisation_Data_Cleaning_Primary_Secondary_Share.R- R script containing code used to clean the data. Folder: Data documentation File: Information Leaflet Data Documentation.pdf 5. METHODS ---------- The full methods of this paper are described in the following publications: Sophie M. C. Green, Louise H. Hall, David P. French, Nikki Rousseau, Catherine Parbutt, Rebecca Walwyn, Samuel G. Smith, on behalf of the ROSETA investigators. Optimization of an information leaflet to support medication beliefs in women with breast cancer: a randomized factorial experiment. 2023. Annals of Behavioral Medicine. 57(11), 988-1000. https://doi.org/10.1093/abm/kaad037 Sophie M. C. Green, Samuel G. Smith, Linda M. Collins, Jillian C. Strayhorn. Decision-making in the multiphase optimization strategy (MOST): Applying decision analysis for intervention value efficiency (DAIVE) to optimize an information leaflet to promote key antecedents of medication adherence. Translational Behavioral Medicine.