Evidence map›Paper›PMID 40894132›Full record

ArticlemedRxiv : the preprint server for health sciences2025

Who Makes It Through the Funnel? Sociodemographic Drivers of Recruitment Completion into an AI-Ready Digital Speech Bank.

Peyman Nejat, Ashley D Bachman, Vicki M Stubbs, Joseph R Duffy, John L Stricker, Vitaly Herasevich, David T Jones, Rene L Utianski, Hugo Botha

Abstract readPreprint
In one paragraph

Article in medRxiv : the preprint server for health sciences, 2025. 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
–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

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

9 authors.

Peyman NejatDepartment of Anesthesiology and Perioperative Medicine, Mayo Clinic, Rochester, Minnesota, USA.ORCID 0000-0001-9223-7942
Ashley D BachmanDepartment of Neurology, Mayo Clinic, Rochester, MN, USA.
Vicki M StubbsDepartment of Neurology, Mayo Clinic, Rochester, MN, USA.
Joseph R DuffyDepartment of Neurology, Mayo Clinic, Rochester, MN, USA.ORCID 0000-0001-8206-3432
John L StrickerDepartment of Information Technologies, Mayo Clinic, Rochester, Minnesota, USA.ORCID 0009-0000-5349-8541
Vitaly HerasevichDepartment of Anesthesiology and Perioperative Medicine, Mayo Clinic, Rochester, Minnesota, USA.ORCID 0000-0002-0214-0651
David T JonesDepartment of Neurology, Mayo Clinic, Rochester, MN, USA.ORCID 0000-0002-4807-9833
Rene L UtianskiDepartment of Neurology, Mayo Clinic, Rochester, MN, USA.ORCID 0000-0001-9519-1302
Hugo BothaOffice of Digital Innovation, Center for Clinical And Translational Science, Mayo Clinic, Rochester, Minnesota, USA.ORCID 0000-0003-4390-685X

Funding

Mayo Clinic Center for Clinical and Translational Science (CCaTS UL1 Supplement - Dr. Timothy Curry)UL1TR002377 · NCATS · MAYO CLINIC ROCHESTER · PI VESNA D GAROVIC · 2017 to 2026
$78.4M
Automated speech assessment for diagnosis of FTD spectrum disordersR01AG083832 · NIA · MAYO CLINIC ROCHESTER · PI Hugo Botha · 2024 to 2026
$2.4M
NCATS NIH HHS UL1 TR002377NIA NIH HHS R01 AG083832
6 · The paper itself

Abstract

Background: Digital recruitment methods offer promising opportunities to address persistent challenges in clinical research participation, particularly in specialized fields like neurology. However, the impact of digital approaches across different socioeconomic and demographic groups remains inadequately understood. This study analyzed participant recruitment pathways in a digital neurology research study to identify sociodemographic factors associated with participation outcomes. Methods: We conducted a longitudinal analysis of 5,846 patients invited to participate in a remote speech capture study for neurological disease research between March and July 2024. Using data from Qualtrics, PTrax, and our recording platform, we tracked participant progression through multiple recruitment checkpoints. Socioeconomic status was assessed using the Housing-based Socioeconomic Status (HOUSES) index and Area Deprivation Index (ADI). We examined associations between participation pathways and demographic factors including age, sex, geographic location, and socioeconomic indices using Kruskal-Wallis and Wilcoxon rank-sum tests. Results: Only 415 participants (7.1%) completed all study requirements. Participants from neighborhoods with higher socioeconomic disadvantage (higher ADI national ranks) were significantly less likely to express interest in initial invitations (median ADI 45.0 vs. 42.0 for responders, Conclusions: Digital recruitment methods in neurological research do not automatically overcome traditional barriers to participation and may introduce new disparities related to the digital divide. The significant associations between participation outcomes and sociodemographic factors-particularly neighborhood socioeconomic status, geographic location, age, and sex-highlight the need for targeted recruitment strategies. Researchers should implement multi-channel approaches, design age-specific engagement strategies, address geographic disparities, and consider socioeconomic factors to enhance the inclusivity and effectiveness of digital recruitment in neurological research.

Indexed as

digital divideDigital recruitmentneurological researchparticipation disparitiessociodemographic factors

Identifiers

PMID40894132
PMCPMC12393637

What OpenQuestion holds

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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.