ArticleJMIR research protocols2018
Monitoring Twitter Conversations for Targeted Recruitment in Cancer Trials in Los Angeles County: Protocol for a Mixed-Methods Pilot Study.
Article in JMIR research protocols, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. It is linked to trial NCT03408561 (The Use of Social Media Listening for Targeted Recruitment of Twitter Users in LA County in Cancer Trials Compared to Historic Recruitment Data), which is not on this map. Cited by 7 papers.
What it found
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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.
The Use of Social Media Listening for Targeted Recruitment of Twitter Users in LA County in Cancer Trials Compared to Historic Recruitment Data: A Mixed-Methods Study
Who cites it
7 citing papers in PubMed, 17 citations in OpenAlex.
- Using Social Media to Facilitate Communication About Women's Testing: Tool Validation Study.JMIR formative research · 2022Article
- Using Patient-Generated Health Data From Twitter to Identify, Engage, and Recruit Cancer Survivors in Clinical Trials in Los Angeles County: Evaluation of a Feasibility Study.JMIR formative research · 2021Article
- Expressed Symptoms and Attitudes Toward Using Twitter for Health Care Engagement Among Patients With Lupus on Social Media: Protocol for a Mixed Methods Study.JMIR research protocols · 2021Article
- Towards Automatic Bot Detection in Twitter for Health-related Tasks.AMIA Joint Summits on Translational Science proceedings. AMIA Joint Summits on Translational Science · 2020Article
- Seeing Is Believing? Unique Capabilities of Internet-Only Studies as a Tool for Implementation Research on HIV Prevention for Men Who Have Sex With Men: A Review of Studies and Methodological Considerations.Journal of acquired immune deficiency syndromes (1999) · 2019Article
- Public Concern About Monitoring Twitter Users and Their Conversations to Recruit for Clinical Trials: Survey Study.Journal of medical Internet research · 2019Article
- Physician Perceptions of the Use of Social Media for Recruitment of Patients in Cancer Clinical Trials.JAMA network open · 2019Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
9 authors at 1 institution in 1 country.
Funding
Abstract
backgroundInsufficient recruitment of participants remains a critical roadblock to successful clinical research, particularly clinical trials. Social media provide new ways for connecting potential participants with research opportunities. Researchers suggest that the social network Twitter may serve as a rich avenue for exploring how patients communicate about their health issues and increasing enrollment in cancer clinical trials. However, there is a lack of evidence that Twitter offers practical utility and impact.
objectiveThis pilot study aimed to examine the feasibility and impact of using Twitter monitoring data (ie, user activity and their conversations about cancer-related conditions and concerns expressed by Twitter users in Los Angeles County) as a tool for enhancing clinical trial recruitment at a comprehensive cancer center.
methodsWe will conduct a mixed-methods interrupted time series study design with a before-and-after social media recruitment intervention. On the basis of a preliminary analysis of eligible trials, we plan to onboard at least 84 clinical trials across 6 disease categories: breast cancer, colon cancer, kidney cancer, lymphoma, non-small cell lung cancer, and prostate cancer that are open to accrual at the University of Southern California (USC) Norris Comprehensive Cancer Center. We will monitor messages about these 6 cancer conditions posted by Twitter users in Los Angeles County. Recruitment for the trials will occur through the Twitter account (@USCTrials). Primary study outcomes-feasibility and acceptance of the social media intervention among targeted Twitter users and the study teams of the onboarded trials-will be assessed using qualitative interviews and the 4-point Likert scale and by calculating the proportion of targeted Twitter users who engaged with outreach messages. Second, impact of the social media intervention will be measured by calculating the proportion of enrollees in trials. The enrollment rate will be compared between the active intervention period and the prior 10 months as historical control for each disease trial group. This study has been funded by the National Center for Advancing Translational Science through a Clinical and Translational Science Award. Study approval was obtained from the clinical investigations committee at USC Norris and the institutional review board at USC.
resultsRecruitment on Twitter started in February 2018. Data collection will be completed in November 2018.
conclusionsThis pilot project will provide preliminary data and practical insight into the application of publicly available Twitter data to identify and recruit clinical trial participants across 6 cancer disease types. We will shed light on the acceptance of the social media intervention among Twitter users and study team members of the onboarded trials. If successful, the findings will inform a multisite randomized controlled trial to determine the efficacy of the social media intervention across different locations and populations.
trial registrationClinicalTrials.gov NCT03408561; https://clinicaltrials.gov/ct2/show/NCT03408561 (Archived by WebCite at http://www.webcitation.org/72LihauzW). REGISTERED REPORT IDENTIFIER: RR1-10.2196/9762.
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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.