ArticleNIHR open research2024
Dataset for a randomised factorial experiment to optimise an information leaflet for women with breast cancer.
Article in NIHR open research, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
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The trial behind it
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Who cites it
1 citing paper in PubMed.
- Dataset for a randomised factorial experiment to optimise an information leaflet for women with breast cancer.NIHR open research · 2024Article
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Authors and funding
2 authors.
Funding
No grant is acknowledged in the PubMed record.
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 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.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.