Dataset for a randomised factorial experiment to optimise an information leaflet for women with breast cancer

NIHR Open Res. 2024 May 30:4:32. doi: 10.3310/nihropenres.13547.1. eCollection 2024.

Abstract

Background: Adherence to adjuvant endocrine therapy (AET) is low in women with breast cancer, which increases the risk of recurrence and mortality. A consistently reported barrier to adherence is low perceived necessity of AET and high concerns. Existing interventions to support medication beliefs have mixed effectiveness and rarely target medication beliefs specifically. We developed an information leaflet with five candidate components aiming to increase necessity beliefs about AET and reduce concerns; (1) diagrams explaining how AET works; (2) icon arrays displaying the benefits of AET; (3) information about the prevalence of side-effects; (4) answers to common concerns and (5) quotes and pictures from breast cancer survivors. Guided by the multiphase optimisation strategy (MOST), we aimed to optimise the content of the information leaflet. We planned for the dataset to be open access to provide an exemplar for other investigators to use.

Methods: The content of the leaflet was optimised in a fully powered online 2 5 factorial experiment. Each candidate component of the leaflet was operationalised as a factor with two levels; on vs off or enhanced vs basic. Healthy women (n=1604) completed the beliefs about medicines questionnaire and were randomised to view one of 32 versions of the information leaflet. The 32 versions comprised unique combinations of the factor levels corresponding to the five candidate intervention components. Time spent on the information leaflet page of the survey was recorded. After viewing the information leaflet, participants completed the beliefs about medicines questionnaire again, a true/false questionnaire assessing their objective knowledge of AET, a subjective rating of their knowledge of AET, and a questionnaire evaluating their satisfaction with the information they received.

Importance of this dataset: The factorial dataset provides the opportunity for other investigators interested in using the MOST framework to learn about complex factorial designs, using a real dataset.

Keywords: breast cancer; factorial; information leaflet; intervention optimisation; multiphase optimisation strategy.

Plain language summary

Most women with breast cancer are treated with adjuvant endocrine therapy (AET) to reduce the chance of breast cancer coming back. However, many women do not take the medication as recommended. Women’s beliefs about the medication are a common reason for not taking AET. Some women do not think AET will help them, and some women have lots of concerns about AET. At the moment, we do not know the best way to change women’s beliefs about AET. Therefore, we ran a study to help us understand what combination of information might help change women’s beliefs about AET. We developed a written information leaflet with five parts; (1) diagrams about how AET works; (2) visual figures of the benefits of AET; (3) information about how likely each side-effect is; (4) answers to common concerns about AET; and (5) pictures and quotes from women who have taken AET. In an online survey, 1,604 healthy women answered questions about their beliefs about the medication. Each woman was shown one version of the information leaflet picked at random. There were 32 possible versions of the information leaflet, which contained unique combinations of the five parts of the leaflet. After women read the leaflet, they were asked to complete the same questionnaire about their beliefs about the medication. They were also asked questions about how satisfied they were with the information they received, true or false questions about AET to assess their knowledge after reading the leaflet, and a rating of how informed they felt about AET. We also recorded how long women spent looking at the leaflet. One of our aims was to make the dataset from this experiment openly available so other scientists could use it to learn how to conduct similar experiments.

Grants and funding

This project is funded by the National Institute for Health and Care Research under a NIHR Advanced Fellowship to Prof Samuel Smith (Grant Reference Number NIHR300588). Smith also acknowledges funding support from a Yorkshire Cancer Research University Academic Fellowship. S.G. 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.