Trial reportJournal of medical Internet research2021
Perceptions of Racially and Ethnically Diverse Women at High Risk of Breast Cancer Regarding the Use of a Web-Based Decision Aid for Chemoprevention: Qualitative Study Nested Within a Randomized Controlled Trial.
Trial report in Journal of medical Internet research, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers, 1 of them a synthesis that pooled it.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
7 citing papers in PubMed, 1 synthesis or guideline pooled it, 9 citations in OpenAlex.
- Pooled it
- Use of web-based decision support to improve informed choice for chemoprevention: a qualitative analysis of pre-implementation interviews (SWOG S1904).BMC medical informatics and decision making · 2024Trial
- Pilot trial of iBDecide: Evaluating an online tool to facilitate shared decision making for adolescents and young adults with ulcerative colitis.Health expectations : an international journal of public participation in health care and health policy · 2022Trial
- Impact of Affordable Care Act Provisions on the Racial Makeup of Patients Enrolled at a Deep South, High-Risk Breast Cancer Clinic.Journal of racial and ethnic health disparities · 2025Article
- Systematic review to understand users perspectives on AI-enabled decision aids to inform shared decision making.NPJ digital medicine · 2024Article
- Developing decision support tools for high-risk women and healthcare providers to increase chemoprevention informed choice and uptake: A retrospective translational science case study.Journal of clinical and translational science · 2024Article
- A Scoping Review of Personalized, Interactive, Web-Based Clinical Decision Tools Available for Breast Cancer Prevention and Screening in the United States.MDM policy & practiceArticle
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
6 authors at 2 institutions in 1 country.
Funding
Abstract
backgroundChemopreventive agents such as selective estrogen receptor modulators and aromatase inhibitors have proven efficacy in reducing breast cancer risk by 41% to 79% in high-risk women. Women at high risk of developing breast cancer face the complex decision of whether to take selective estrogen receptor modulators or aromatase inhibitors for breast cancer chemoprevention. RealRisks is a patient-centered, web-based decision aid (DA) designed to promote the understanding of breast cancer risk and to engage diverse women in planning a preference-sensitive course of decision making about taking chemoprevention.
objectiveThis study aims to understand the perceptions of women at high risk of developing breast cancer regarding their experience with using RealRisks-a DA designed to promote the uptake of breast cancer chemoprevention-and to understand their information needs.
methodsWe completed enrollment to a randomized controlled trial among 300 racially and ethnically diverse women at high risk of breast cancer who were assigned to standard educational materials alone or such materials in combination with RealRisks. We conducted semistructured interviews with a subset of 21 high-risk women enrolled in the intervention arm of the randomized controlled trial who initially accessed the tool (on average, 1 year earlier) to understand how they interacted with the tool. All interviews were audio recorded, transcribed verbatim, and compared with digital audio recordings to ensure the accuracy of the content. We used content analysis to generate themes.
resultsThe mean age of the 21 participants was 58.5 (SD 10.1) years. The participants were 5% (1/21) Asian, 24% (5/21) Black or African American, and 71% (15/21) White; 10% (2/21) of participants were Hispanic or Latina. All participants reported using RealRisks after being granted access to the DA. In total, 4 overarching themes emerged from the qualitative analyses: the acceptability of the intervention, specifically endorsed elements of the DA, recommendations for improvements, and information needs. All women found RealRisks to be acceptable and considered it to be helpful (21/21, 100%). Most women (13/21, 62%) reported that RealRisks was easy to navigate, user-friendly, and easily accessible on the web. The majority of women (18/21, 86%) felt that RealRisks improved their knowledge about breast cancer risk and chemoprevention options and that RealRisks informed their (17/21, 81%) decision about whether or not to take chemoprevention. Some women (9/21, 43%) shared recommendations for improvements, as they wanted more tailoring based on user characteristics, felt that the DA was targeting a narrow population of Hispanic or Latina by using graphic novel-style narratives, wanted more understandable terminology, and felt that the tool placed a strong emphasis on chemoprevention drugs.
conclusionsThis qualitative study demonstrated the acceptability of the RealRisks web-based DA among a diverse group of high-risk women, who provided some recommendations for improvement.
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What OpenQuestion holds
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.