Evidence map›Paper›PMID 41813231›Full record

ArticleJournal of medical Internet research2026

Leveraging Social Media to Achieve Population-Level Reach of Lung Cancer Screening-Eligible Individuals: A RE-AIM Framework Perspective.

Lisa Carter-Bawa, Jamie S Ostroff, Susan M Rawl, Erin A Hirsch, Smita C Banerjee, Andrew Ciupek, Robert Skipworth Comer, Minal Kale, Katherine T Leopold, Patrick O Monahan and 4 more

Abstract read
In one paragraph

Article in Journal of medical Internet research, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from 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.

2 · The registry

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.

3 · Its place in the literature

Who cites it

1 citing paper in PubMed.

  1. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

14 authors.

Lisa Carter-BawaCancer Prevention Precision Control Institute, Center for Discovery & Innovation, Hackensack Meridian Health, 123 Metro Blvd, 6th Floor, 6400 Pod, Nutley, NJ, 07110, United States, 1 646-246-2118.ORCID http://orcid.org/0000-0002-6448-6033
Jamie S OstroffDepartment of Psychiatry & Behavioral Science, Memorial Sloan Kettering Cancer Center, New York, NY, United States.ORCID http://orcid.org/0000-0003-2671-5680
Susan M RawlSchool of Nursing, Indiana University, Indianapolis, United States.ORCID http://orcid.org/0000-0003-2052-2853
Erin A HirschCancer Prevention Precision Control Institute, Center for Discovery & Innovation, Hackensack Meridian Health, 123 Metro Blvd, 6th Floor, 6400 Pod, Nutley, NJ, 07110, United States, 1 646-246-2118.ORCID http://orcid.org/0000-0002-3289-5422
Smita C BanerjeeDepartment of Psychiatry & Behavioral Science, Memorial Sloan Kettering Cancer Center, New York, NY, United States.ORCID http://orcid.org/0000-0001-5479-5978
Andrew CiupekGo2 for Lung Cancer, Washington, United States.ORCID http://orcid.org/0000-0003-4203-3946
Robert Skipworth ComerSchool of Informatics, Indiana University, Indianapolis, United States.ORCID http://orcid.org/0000-0003-3839-1361
Minal KaleDepartment of Medicine, Internal Medicine, Icahn School of Medicine at Mount Sinai, New York, United States.ORCID http://orcid.org/0000-0003-0544-7429
Katherine T LeopoldHackensack Meridian School of Medicine, Hackensack Meridian Health, Nutley, United States.ORCID http://orcid.org/0009-0004-7851-8958
Patrick O MonahanDepartment of Biostatistics and Health Data Science, Indiana University School of Medicine, Indianapolis, IN, United States.ORCID http://orcid.org/0000-0001-9481-1995
James E SlavenDepartment of Biostatistics and Health Data Science, Indiana University School of Medicine, Indianapolis, IN, United States.ORCID http://orcid.org/0000-0002-6744-3821
Francis ValenzonaCancer Prevention Precision Control Institute, Center for Discovery & Innovation, Hackensack Meridian Health, 123 Metro Blvd, 6th Floor, 6400 Pod, Nutley, NJ, 07110, United States, 1 646-246-2118.ORCID http://orcid.org/0009-0008-6215-3789
Renda Soylemez WienerCenter for Health Optimization & Implementation Research, VA Boston Healthcare System, Boston, MA, United States.ORCID http://orcid.org/0000-0001-7712-2135
Ana Guadalupe VielmaCancer Prevention Precision Control Institute, Center for Discovery & Innovation, Hackensack Meridian Health, 123 Metro Blvd, 6th Floor, 6400 Pod, Nutley, NJ, 07110, United States, 1 646-246-2118.ORCID http://orcid.org/0009-0009-2196-0987

Funding

X-RAY CRYSTALLOGRAPHYP30CA008748 · NCI · SLOAN-KETTERING INSTITUTE FOR CANCER RES · PI SELWYN M VICKERS · 1985 to 2026
$347.4M
NCI NIH HHS P30 CA008748
6 · The paper itself

Abstract

Background: Annual lung cancer screening (LCS) can decrease lung cancer-related mortality by finding cancer at earlier, more treatable stages, yet uptake remains abysmally low in the United States, especially among adults who seldom interact with the health system. Many eligible individuals are unaware that LCS exists, underscoring the critical need for scalable, population-level communication strategies that increase awareness and engagement. Objective: The aim of this study was to evaluate reach, as defined by the Reach, Effectiveness, Adoption, Implementation, and Maintenance framework, as the extent to which the target population comes in contact with a social media-based strategy, Facebook-targeted advertisement (FBTA), designed to connect LCS-eligible individuals in the United States with a digital health communication message. The advertisement served as a digital outreach strategy for promoting engagement with LungTalk, an evidence-based intervention aimed at increasing awareness and informed decision-making about LCS. Methods: As part of the INSPIRE-Lung Study (INnovating Social Media for Prevention: LUNG Cancer Screening Awareness, Knowledge, and Uptake), 5 FBTA campaigns were launched over a 79-day period throughout the United States. Advertisements targeted adults aged 50-80 years with interests related to smoking or smoking cessation and linked to a study website where participants could complete an eligibility screener and learn more about the trial. Facebook analytics were used to assess reach, defined by the number, proportion, and demographic characteristics of individuals exposed to and interacting with FBTA content. Key metrics included total reach, impressions, link clicks, and cost-efficiency. Results: The FBTA campaigns reached 1,048,191 unique users and generated 3,109,482 impressions (total advertisement displays, including repeat exposures to the same user). A total of 24,816 individuals clicked on the advertisements (2.37% click-through rate), and 7117 completed the eligibility screener. Of those eligible, 1272 (17.9%) met lung screening criteria, and of these, 483 (38% participation rate) enrolled in the trial. The cost per click was US $0.40, and the cost per enrolled participant was US $19.46. Individuals reached via FBTA were demographically diverse and included many who may be disconnected from traditional health care systems. Conclusions: FBTA is a scalable, cost-effective strategy to achieve population-level reach of LCS-eligible adults. By conceptualizing reach as exposure to an upstream digital message rather than enrollment alone, this study illustrates how social media can broaden population access to evidence-based cancer prevention tools such as LungTalk. Future research should explore embedding intervention content directly into social media platforms and tracking downstream clinical outcomes.

Indexed as

Early Detection of CancerLung NeoplasmsSocial MediaAgedFemaleHumansMaleMedia ExposureMiddle AgedUnited Statesdigital health communicationFacebook-targeted advertisementimplementation sciencelung cancer screeningpublic health outreachReach, Effectiveness, Adoption, Implementation, and MaintenanceRE-AIM

Identifiers

PMID41813231
PMCPMC12978886

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

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.