Evidence map›Paper›PMID 39173153›Full record

ArticleJMIR formative research2024

Recruitment in Appalachian, Rural and Older Adult Populations in an Artificial Intelligence World: Study Using Human-Mediated Follow-Up.

Tabitha Milliken, Donielle Beiler, Samantha Hoffman, Ashlee Olenginski, Vanessa Troiani

Abstract read
In one paragraph

Article in JMIR formative research, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing 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

2 citing papers in PubMed.

  1. Article
  2. 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

5 authors.

Tabitha MillikenResearch Institute, Geisinger, Danville, PA, United States.ORCID 0000-0002-3311-6013
Donielle BeilerResearch Institute, Geisinger, Danville, PA, United States.ORCID 0000-0002-4570-631X
Samantha HoffmanResearch Institute, Geisinger, Danville, PA, United States.ORCID 0000-0003-2666-7594
Ashlee OlenginskiPhiladelphia College of Osteopathic Medicine, Philadelphia, PA, United States.ORCID 0009-0008-4412-3172
Vanessa TroianiResearch Institute, Geisinger, Danville, PA, United States.ORCID 0000-0001-6689-8157

Funding

Supplemental Application for Clinical and Genetic Risk Profile of Opioid Use DisorderR01DA044015 · NIDA · GEISINGER CLINIC · PI BERRETTINI, WADE H, ROBISHAW, JANET D · 2017 to 2021
$4.5M
NIDA NIH HHS R01 DA044015
6 · The paper itself

Abstract

backgroundParticipant recruitment in rural and hard-to-reach (HTR) populations can present unique challenges. These challenges are further exacerbated by the need for low-cost recruiting, which often leads to use of web-based recruitment methods (eg, email, social media). Despite these challenges, recruitment strategy statistics that support effective enrollment strategies for underserved and HTR populations are underreported. This study highlights how a recruitment strategy that uses email in combination with follow-up, mostly phone calls and email reminders, produced a higher-than-expected enrollment rate that includes a diversity of participants from rural, Appalachian populations in older age brackets and reports recruitment and demographic statistics within a subset of HTR populations.

objectiveThis study aims to provide evidence that a recruitment strategy that uses a combination of email, telephonic, and follow-up recruitment strategies increases recruitment rates in various HTR populations, specifically in rural, older, and Appalachian populations.

methodsWe evaluated the overall enrollment rate of 1 recruitment arm of a larger study that aims to understand the relationship between genetics and substance use disorders. We evaluated the enrolled population's characteristics to determine recruitment success of a combined email and follow-up recruitment strategy, and the enrollment rate of HTR populations. These characteristics included (1) enrollment rate before versus after follow-up; (2) zip code and county of enrollee to determine rural or urban and Appalachian status; (3) age to verify recruitment in all eligible age brackets; and (4) sex distribution among age brackets and rural or urban status.

resultsThe email and follow-up arm of the study had a 17.4% enrollment rate. Of the enrolled participants, 76.3% (4602/6030) lived in rural counties and 23.7% (1428/6030) lived in urban counties in Pennsylvania. In addition, of patients enrolled, 98.7% (5956/6030) were from Appalachian counties and 1.3% (76/6030) were from non-Appalachian counties. Patients from rural Appalachia made up 76.2% (4603/6030) of the total rural population. Enrolled patients represented all eligible age brackets from ages 20 to 75 years, with the 60-70 years age bracket having the most enrollees. Females made up 72.5% (4371/6030) of the enrolled population and males made up 27.5% (1659/6030) of the population.

conclusionsResults indicate that a web-based recruitment method with participant follow-up, such as a phone call and email follow-up, increases enrollment numbers more than web-based methods alone for rural, Appalachian, and older populations. Adding a humanizing component, such as a live person phone call, may be a key element needed to establish trust and encourage patients from underserved and rural areas to enroll in studies via web-based recruitment methods. Supporting statistics on this recruitment strategy should help researchers identify whether this strategy may be useful in future studies and HTR populations.

Indexed as

Artificial IntelligencePatient SelectionRural PopulationAdultAgedAppalachian RegionElectronic MailFemaleFollow-Up StudiesHumansMaleMiddle AgedYoung Adultagingenrollment rateNorthern Appalachiarecruitmentruraltelecommunicationweb-based

Identifiers

PMID39173153
PMCPMC11377916

What OpenQuestion holds

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LicenceCC BY
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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.