Evidence map›Paper›PMID 33898596›Full record

ArticlemHealth2021

Online recruitment of youth for mHealth studies.

Maria Zlotorzynska, José A Bauermeister, Jesse M Golinkoff, Willey Lin, Travis H Sanchez, Lisa Hightow-Weidman

Open access · diamondAbstract read
In one paragraph

Article in mHealth, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 22 papers, 2 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
22citing papers in PubMed, 2 pooled it
3.4field-weighted citation impact, top 7% of its field
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

22 citing papers in PubMed, 2 syntheses or guidelines pooled it, 34 citations in OpenAlex.

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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

6 authors at 3 institutions in 1 country.

Maria ZlotorzynskaRollins School of Public Health, Emory University, Atlanta, GA, USA.
José A BauermeisterSchool of Nursing, University of Pennsylvania, 418 Curie Blvd, Philadelphia, PA, USA.
Jesse M GolinkoffSchool of Nursing, University of Pennsylvania, 418 Curie Blvd, Philadelphia, PA, USA.
Willey LinSchool of Nursing, University of Pennsylvania, 418 Curie Blvd, Philadelphia, PA, USA.
Travis H SanchezRollins School of Public Health, Emory University, Atlanta, GA, USA.
Lisa Hightow-WeidmanInstitute of Global Health and Infectious Diseases, University of North Carolina at Chapel Hill, 130 Mason Farm Rd, Chapel Hill, NC, USA.
University of Pennsylvania · USEmory University · USUniversity of North Carolina at Chapel Hill · US

Funding

The UNC/Emory Center for Innovative Technology (iTech) across the prevention and care continuumU19HD089881 · NICHD · UNIV OF NORTH CAROLINA CHAPEL HILL · PI HIGHTOW-WEIDMAN, LISA B, SULLIVAN, PATRICK SEAN · 2016 to 2022
$35.8M
NICHD NIH HHS U19 HD089881
6 · The paper itself

Abstract

backgroundSocial networking sites and apps have emerged as an opportunity to engage in research young men who have sex with men (YMSM) at risk of HIV infection who may not be otherwise reached by in-person recruitment efforts. This paper highlights lessons learned, best practices and on-going recruitment challenges in the iTech network of the NIH Adolescent Trials Network for HIV Interventions.

methodsRecruitment was conducted for four randomized controlled trials (RCTs) of mHealth HIV prevention interventions for YMSM living in 10 US cities. Advertising was purchased on Facebook, Instagram, Snapchat, Twitter and Grindr. Users who clicked on banner ads were taken directly to a study-specific eligibility screener and if eligible, were asked to provide contact information for follow-up by respective study site staff. Ad and screening metrics (impressions, clicks, cost per click (CPC), click-through rate (CTR), number screened, number eligible, number who provided contact information and cost per eligible contact) were compared across platforms, studies and geographic areas (where available). Screening metrics were also calculated for in-person recruitment efforts.

resultsGrindr and Snapchat ads produced the highest CTRs as compared to Facebook ads. However, these ads had the lowest proportions of users who initiated eligibility screeners and ultimately Facebook ads yielded the lowest cost per eligible contact across studies. Instagram ads yielded the highest proportions of eligible contacts who were racial/ethnic minorities and under the age of 18. Geographic variability in cost per eligible contact was observed for studies with identical eligibility criteria running concurrently in different regions, driven by both advertising costs and the screening and eligibility rates. Despite lower eligibility rates, the total numbers of eligible contacts were higher for online advertising campaigns, as compared to other recruitment efforts, for all studies except P3. Ads recruiting for P3 had the highest cost per eligible contact, likely due to this study having the most stringent eligibility criteria of the studies described.

conclusionsWe implemented a successful online advertising strategy to recruit YMSM at high risk for HIV infection into four RCTs of mHealth interventions. This report provides a framework for evaluation of data from future online recruitment efforts across platforms and geographic areas, regardless of inevitable changes in the digital marketing space.

Indexed as

AdolescentadvertisingHIVsocial media

Identifiers

PMID33898596
PMCPMC8063013
OpenAlexW3046084150

What OpenQuestion holds

Textmetadata
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.