Evidence map›Paper›PMID 39808785›Full record

ArticleJournal of medical Internet research2025

Recruiting Young People for Digital Mental Health Research: Lessons From an AI-Driven Adaptive Trial.

Wu Yi Zheng, Artur Shvetcov, Aimy Slade, Zoe Jenkins, Leonard Hoon, Alexis Whitton, Rena Logothetis, Smrithi Ravindra, Stefanus Kurniawan, Sunil Gupta and 13 more

Abstract readAdaptive Clinical Trial
In one paragraph

Article in Journal of medical Internet research, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
4citing papers in PubMed, 1 pooled it
–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

4 citing papers in PubMed, 1 synthesis or guideline pooled it.

  1. Pooled it
  2. Observational
  3. Article
  4. 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

23 authors.

Wu Yi ZhengBlack Dog Institute, University of New South Wales, Sydney, Australia.ORCID 0000-0002-1159-4700
Artur ShvetcovBlack Dog Institute, University of New South Wales, Sydney, Australia.ORCID 0000-0003-0592-984X
Aimy SladeBlack Dog Institute, University of New South Wales, Sydney, Australia.ORCID 0000-0001-5368-1792
Zoe JenkinsBlack Dog Institute, University of New South Wales, Sydney, Australia.ORCID 0009-0002-8749-7939
Leonard HoonApplied Artificial Intelligence Institute, Deakin University, Melbourne, Australia.ORCID 0000-0003-0428-7240
Alexis WhittonBlack Dog Institute, University of New South Wales, Sydney, Australia.ORCID 0000-0002-7944-2172
Rena LogothetisApplied Artificial Intelligence Institute, Deakin University, Melbourne, Australia.ORCID 0000-0003-0143-3812
Smrithi RavindraBlack Dog Institute, University of New South Wales, Sydney, Australia.ORCID 0000-0001-7551-8179
Stefanus KurniawanApplied Artificial Intelligence Institute, Deakin University, Melbourne, Australia.ORCID 0009-0001-4469-1056
Sunil GuptaApplied Artificial Intelligence Institute, Deakin University, Melbourne, Australia.ORCID 0000-0002-3308-1930
Kit HuckvaleCentre for Digital Transformation of Health, University of Melbourne, Melbourne, Australia.ORCID 0000-0001-9088-6682
Eileen StechBlack Dog Institute, University of New South Wales, Sydney, Australia.ORCID 0000-0003-0151-9026
Akash AgarwalApplied Artificial Intelligence Institute, Deakin University, Melbourne, Australia.ORCID 0009-0009-2333-8366
Joost Funke KupperApplied Artificial Intelligence Institute, Deakin University, Melbourne, Australia.ORCID 0009-0009-6067-1782
Stuart CameronApplied Artificial Intelligence Institute, Deakin University, Melbourne, Australia.ORCID 0000-0002-7862-3051
Jodie RosenbergBlack Dog Institute, University of New South Wales, Sydney, Australia.ORCID 0000-0002-8738-3467
Nicholas ManoglouApplied Artificial Intelligence Institute, Deakin University, Melbourne, Australia.ORCID 0009-0004-2225-149X
Manisha SenadeeraApplied Artificial Intelligence Institute, Deakin University, Melbourne, Australia.ORCID 0000-0002-5923-6857
Svetha VenkateshApplied Artificial Intelligence Institute, Deakin University, Melbourne, Australia.ORCID 0000-0001-8675-6631
Kon MouzakisApplied Artificial Intelligence Institute, Deakin University, Melbourne, Australia.ORCID 0000-0003-4447-5166
Rajesh VasaApplied Artificial Intelligence Institute, Deakin University, Melbourne, Australia.ORCID 0000-0003-4805-1467
Helen ChristensenBlack Dog Institute, University of New South Wales, Sydney, Australia.ORCID 0000-0003-0435-2065
Jill M NewbyBlack Dog Institute, University of New South Wales, Sydney, Australia.ORCID 0000-0002-6473-9811

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundWith increasing adoption of remote clinical trials in digital mental health, identifying cost-effective and time-efficient recruitment methodologies is crucial for the success of such trials. Evidence on whether web-based recruitment methods are more effective than traditional methods such as newspapers, media, or flyers is inconsistent. Here we present insights from our experience recruiting tertiary education students for a digital mental health artificial intelligence-driven adaptive trial-Vibe Up.

objectiveWe evaluated the effectiveness of recruitment via Facebook and Instagram compared to traditional methods for a treatment trial and compared different recruitment methods' retention rates. With recruitment coinciding with COVID-19 lockdowns across Australia, we also compared the cost-effectiveness of social media recruitment during and after lockdowns.

methodsRecruitment was completed for 2 pilot trials and 6 minitrials from June 2021 to May 2022. To recruit participants, paid social media advertising on Facebook and Instagram was used, alongside mailing lists of university networks and student organizations or services, media releases, announcements during classes and events, study posters or flyers on university campuses, and health professional networks. Recruitment data, including engagement metrics collected by Meta (Facebook and Instagram), advertising costs, and Qualtrics data on recruitment methods and survey completion rates, were analyzed using RStudio with R (version 3.6.3; R Foundation for Statistical Computing).

resultsIn total, 1314 eligible participants (aged 22.79, SD 4.71 years; 1079, 82.1% female) were recruited to 2 pilot trials and 6 minitrials. The vast majority were recruited via Facebook and Instagram advertising (n=1203; 92%). Pairwise comparisons revealed that the lead institution's website was more effective in recruiting eligible participants than Facebook (z=3.47; P=.003) and Instagram (z=4.23; P<.001). No differences were found between recruitment methods in retaining participants at baseline, at midpoint, and at study completion. Wilcoxon tests found significant differences between lockdown (pilot 1 and pilot 2) and postlockdown (minitrials 1-6) on costs incurred per link click (lockdown: median Aus $0.35 [US $0.22], IQR Aus $0.27-$0.47 [US $0.17-$0.29]; postlockdown: median Aus $1.00 [US $0.62], IQR Aus $0.70-$1.47 [US $0.44-$0.92]; W=9087; P<.001) and the amount spent per hour to reach the target sample size (lockdown: median Aus $4.75 [US $2.95], IQR Aus $1.94-6.34 [US $1.22-$3.97]; postlockdown: median Aus $13.29 [US $8.26], IQR Aus $4.70-25.31 [US $2.95-$15.87]; W=16044; P<.001).

conclusionsSocial media advertising via Facebook and Instagram was the most successful strategy for recruiting distressed tertiary students into this artificial intelligence-driven adaptive trial, providing evidence for the use of this recruitment method for this type of trial in digital mental health research. No recruitment method stood out in terms of participant retention. Perhaps a reflection of the added distress experienced by young people, social media recruitment during the COVID-19 lockdown period was more cost-effective.

trial registrationAustralian New Zealand Clinical Trials Registry ACTRN12621001092886; https://tinyurl.com/39f2pdmd; Australian New Zealand Clinical Trials Registry ACTRN12621001223820; https://tinyurl.com/bdhkvucv.

Indexed as

Artificial IntelligenceMental HealthPatient SelectionAdultAustraliaCost-Benefit AnalysisCOVID-19FemaleHumansMalePilot ProjectsSocial MediaYoung Adultartificial intelligenceFacebookrecruitmentretention, COVID-19

Identifiers

PMID39808785
PMCPMC11775482

What OpenQuestion holds

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LicenceCC BY
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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.