ArticleJournal of medical Internet research2019
Attracting Users to Online Health Communities: Analysis of LungCancer.net's Facebook Advertisement Campaign Data.
Article in Journal of medical Internet research, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 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.
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Who cites it
8 citing papers in PubMed.
- Communication strategies driving online health community patient awareness and engagement investigated within atrial fibrillation context.NPJ digital medicine · 2025Article
- Marketing mental health services: a mixed-methods analysis of racially and ethnically diverse college students' engagement with and perspectives on U.S. university mental health clinics' websites.BMC health services research · 2024Article
- Blending citizen science with natural language processing and machine learning: Understanding the experience of living with multiple sclerosis.PLOS digital health · 2023Article
- Exploring Chronic Pain and Pain Management Perspectives: Qualitative Pilot Analysis of Web-Based Health Community Posts.JMIR infodemiology · 2023Article
- Optimizing Rural People's Access to Relevant Cancer Education and Support Using Peer-Led YouTube Films.Journal of cancer education : the official journal of the American Association for Cancer Education · 2022Article
- Article
- Social Media Big Data: The Good, The Bad, and the Ugly (Un)truths.Frontiers in big data · 2021Article
- Assessment of Adaptive Engagement and Support Model for People With Chronic Health Conditions in Online Health Communities: Combined Content Analysis.Journal of medical Internet research · 2020Article
Corrections and comments
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Authors and funding
6 authors.
Funding
Abstract
backgroundWith growing numbers of adults turning to the internet to get answers for health-related questions, online communities provide platforms with participatory networks to deliver health information and social support. However, to optimize the benefits of these online communities, these platforms must market effectively to attract new members and promote community growth.
objectiveThe aim of this study was to assess the engagement results of Facebook advertisements designed to increase membership in the LungCancer.net online community.
methodsIn the fall of 2017, a series of 5 weeklong Facebook advertisement campaigns were launched targeting adults over the age of 18 years with an interest in lung cancer to increase opt ins to the LungCancer.net community (ie, the number of people who provided their email to join the site).
resultsThe advertisements released during this campaign had a sum reach of 91,835 people, and 863 new members opted into the LungCancer.net community by providing their email address. Females aged 55 to 64 years were the largest population reached by the campaign (31,401/91,835; 34.29%), whereas females aged 65 and older were the largest population who opted into the LungCancer.net community (307/863; 35.57%). A total of US $1742 was invested in the Facebook campaigns, and 863 people opted into LungCancer.net, resulting in a cost of US $2.02 per new member.
conclusionsThis research demonstrates the feasibility of using Facebook advertising to promote and grow online health communities. More research is needed to compare the effectiveness of various advertising approaches. Public health professionals should consider Facebook campaigns to effectively connect intended audiences to health information and support.
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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.