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Recruitment in Appalachian, Rural and Older Adult Populations in an Artificial Intelligence World: Study Using Human-Mediated Follow-Up.
Milliken, Tabitha; Beiler, Donielle; Hoffman, Samantha; Olenginski, Ashlee; Troiani, Vanessa.
Afiliación
  • Milliken T; Research Institute, Geisinger, Danville, PA, United States.
  • Beiler D; Research Institute, Geisinger, Danville, PA, United States.
  • Hoffman S; Research Institute, Geisinger, Danville, PA, United States.
  • Olenginski A; Philadelphia College of Osteopathic Medicine, Philadelphia, PA, United States.
  • Troiani V; Research Institute, Geisinger, Danville, PA, United States.
JMIR Form Res ; 8: e38189, 2024 Aug 22.
Article en En | MEDLINE | ID: mdl-39173153
ABSTRACT

BACKGROUND:

Participant 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.

OBJECTIVE:

This 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.

METHODS:

We 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.

RESULTS:

The 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.

CONCLUSIONS:

Results 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.
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Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Asunto principal: Población Rural / Inteligencia Artificial / Selección de Paciente Límite: Adult / Aged / Female / Humans / Male / Middle aged País/Región como asunto: America do norte Idioma: En Revista: JMIR Form Res Año: 2024 Tipo del documento: Article País de afiliación: Estados Unidos Pais de publicación: Canadá

Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Asunto principal: Población Rural / Inteligencia Artificial / Selección de Paciente Límite: Adult / Aged / Female / Humans / Male / Middle aged País/Región como asunto: America do norte Idioma: En Revista: JMIR Form Res Año: 2024 Tipo del documento: Article País de afiliación: Estados Unidos Pais de publicación: Canadá