Evidence map›Paper›PMID 42592455›Full record

ArticlemHealth2026

Leveraging social media to promote targeted advertisements and key messaging for optimal recruitment into clinical research.

Carson J Peters, Quynh C Nguyen, Xin He, Héctor E Alcalá, James Butler, Noah S Triplett, Elizabeth M Norell

Abstract read
In one paragraph

Article in mHealth, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
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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

0 citing papers in PubMed.

No citing paper in PubMed yet.

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

7 authors.

Carson J PetersDepartment of Behavioral and Community Health, School of Public Health, University of Maryland, College Park, MD, USA.ORCID https://orcid.org/0009-0004-1348-1728
Quynh C NguyenNational Institute of Nursing Research (NINR), National Institutes of Health (NIH), Bethesda, MD, USA.
Xin HeDepartment of Epidemiology and Biostatistics, School of Public Health, University of Maryland, College Park, College Park, MD, USA.
Héctor E AlcaláDepartment of Behavioral and Community Health, School of Public Health, University of Maryland, College Park, MD, USA.
James ButlerDepartment of Behavioral and Community Health, School of Public Health, University of Maryland, College Park, MD, USA.
Noah S TriplettDepartment of Behavioral and Community Health, School of Public Health, University of Maryland, College Park, MD, USA.
Elizabeth M NorellDepartment of Behavioral and Community Health, School of Public Health, University of Maryland, College Park, MD, USA.

Funding

Rosie the Chatbot: Leveraging Automated and Personalized Health Information Communication to Reduce Disparities in Maternal and Child HealthR01MD016037 · NIMHD · UNIV OF MARYLAND, COLLEGE PARK · PI NGUYEN, THU, NORELL, ELIZABETH MARIE · 2021 to 2025
$3.3M
HashtagHealthZIANR000043 · NINR · NATIONAL INSTITUTE OF NURSING RESEARCH · PI NGUYEN, QUYNH · 2025 to 2025
$773k
Intramural NIH HHS ZIA NR000043NIMHD NIH HHS R01 MD016037
6 · The paper itself

Abstract

Background: Social media, a strategy that embeds transformative technology and targeted messaging, is promising for optimizing recruitment in the clinical research enterprise. This study aimed to evaluate recruitment rates and outcomes of Facebook advertisements, a social media-based platform, and explored targeted recruitment messaging among a key population. Methods: A convergent parallel mixed-methods design, with secondary data analysis of Facebook advertisements and in-depth, semi-structured interviews (N=15) with recruiters, was conducted. The quantitative analysis included descriptive statistics and bivariate analyses between the conditions of targeted versus general advertisements. The qualitative analysis utilized a five-step reflexive thematic analysis approach. Data analyses were integrated through a joint display. Results: The recruitment rate among those exposed to the targeted advertisements (35%) was higher compared to the general advertisements (27%). A similar trend followed for the interest rate, with targeted advertisements (44%) having higher interest rates than the general advertisements (41%). The thematic analysis generated two themes related to tailored communication and accessible language for recruitment messaging. The integrated joint display elucidated that higher resonance with targeted messaging resulted in better recruitment outcomes. Conclusions: Targeted advertisements resulted in a modestly higher recruitment and interest rate compared to general advertisements on Facebook. Targeted advertisements were nearly twice as cost-effective in recruitment and expressed interest compared to general advertisements. Integrated joint display suggests that targeted advertisements and messaging, with tailored language, enhance recruitment and interest in clinical research with practical significance. Optimizing recruitment strategies is relevant for investigators to reduce obstacles in the clinical research enterprise. Findings suggest this strategy optimizes recruitment for a key population, offering promise for advancing efficient and representative clinical research that can benefit research investigators.

Indexed as

artificial intelligenceFacebookinternetpublic healthSocial media

Identifiers

PMID42592455
PMCPMC13466809

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Registered trials

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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.