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. Author manuscript; available in PMC: 2026 Feb 27.
Published before final editing as: Drug Alcohol Depend. 2025 Sep 23;276:112894. doi: 10.1016/j.drugalcdep.2025.112894

Preferences for clinical trial participation among people who use drugs in rural Oregon and Appalachian Ohio

Kathryn E Lancaster 1,*, Caroline W Koudelka 2, Miriam R Elman 2, Madison N Enderle 1, Sarann Bielavitz 2, Angela T Estadt 3, Ryan R Cook 4, P Todd Korthuis 4, April M Young 5
PMCID: PMC12940545  NIHMSID: NIHMS2130875  PMID: 41005181

Abstract

Background

Rural communities in the United States face significant challenges in participating in clinical trials, despite being heavily impacted by opioid and injection drug use epidemics. Barriers such as transportation, stigma, and limited resources often deter rural people who use drugs (PWUD) from engaging in research. Discrete choice experiments (DCEs) can help identify trial design features that support participation by eliciting participant preferences.

Methods

We selected DCE attributes and attribute levels through literature review and qualitative interviews. Peer-based Retention of People who Use Drugs in Rural Research (PROUD-R2) study participants in rural Oregon and Appalachian Ohio completed the DCE at their baseline visit. We used conditional logit models to estimate preference weights.

Results

Overall, 478 participants completed the DCE and most (71%; n=337) were from Oregon. The majority were male (63%; n=299) and were white (85%; n=404). Overall, transportation support, particularly travel reimbursement (preference weight=0.87; p<0.01; relative utility versus videochat=1.15), was the most valued feature for clinical trial participation. Participants also preferred shorter appointments (relative utility of 1-hour versus 3-hour=0.44) and evening over morning appointments (relative utility=0.29).

Conclusions

Rural PWUD preferences underscore the need to redesign clinical trial protocols with equity and feasibility at the forefront. Direct transportation support emerged as the top priority, reflecting how rural poverty and isolation limit access. Preferences for shorter and later-day appointments suggest a need for low-burden, flexible scheduling. Incorporating participant-centered features can improve trust, enrollment, and retention, ensuring rural PWUD are included in research that addresses their needs.

Trial registration

ClinicalTrials.gov NCT03885024.

Keywords: Substance use, rural, clinical trial, discrete choice experiment, recruitment, retention

1. Introduction

Rural communities have been heavily affected by the opioid and injection drug use epidemics in the United States (Havens et al., 2018; Schalkoff et al., 2021). These epidemics have led to rises in fatal and non-fatal overdoses, hepatitis C virus (HCV) infections, and concerns of human immunodeficiency virus (HIV) outbreaks (Centers for Disease Control and Prevention, 2024; Mack et al., 2017; Zibbell et al., 2018; Van Handel et al., 2016). These increases are particularly alarming in many rural communities given the limited resources for substance use treatment, harm reduction, and HCV and HIV treatment (Bono et al., 2023; Browne et al., 2016; Canary et al., 2017; Cloud et al., 2019; Havens et al., 2018; Pellowski, 2013; Rural Health Information Hub, 2024; Schranz et al., 2018; Walters et al., 2023). To address these growing problems, evidence-based substance use prevention, harm reduction, and treatment programs are urgently needed. Yet, rural populations are often underrepresented in clinical research (Baquet et al., 2006; Feyman et al., 2020; Mapes et al., 2020; Occa et al., 2022). Rural populations must be included in clinical research to effectively implement evidence-based programs addressing the needs of rural people who use drugs (PWUD) and adapted to their circumstances and challenges, including lack of transportation, gaps in healthcare, and stigma (Pellowski, 2013; Rosenblatt et al., 2015; Schafer et al., 2017; Schranz et al., 2018; Thomas et al., 2020; Walters et al., 2022; Westergaard et al., 2015; Winter et al., 2018).

The lack of infrastructure and health care gaps faced by PWUD in rural areas increase their risk of overdoses and vulnerability to HCV and HIV transmission (Havens et al., 2018; Thomas et al., 2020). Rurality also creates challenges to participation and retention of rural PWUD in clinical research. For instance, people in rural areas are less likely to be aware of research opportunities than their urban counterparts (Feuer et al., 2022; Jones et al., 2006; Kim et al., 2014). Additionally, experience with stigma from health care providers may lead rural PWUD to be reluctant to engage with researchers (especially in clinical settings) in anticipation of encountering further stigma (Browne et al., 2016; Carter et al., 2021; Surratt et al., 2021). And PWUD in rural areas may mistrust or fear researchers (Hetrick et al., 2021; Jones et al., 2006; Kim et al., 2014). PWUD in rural areas often struggle to make it to research study appointments due to lack of transportation and/or restrictive schedules (Batista et al., 2016; Davis et al., 2019; Friedman et al., 2015; Hetrick et al., 2021). In longitudinal research, participants’ frequently changing contact information impedes retention (Hetrick et al., 2021). To overcome these barriers to participation and retention in research in rural areas, research protocols should be tailored to the preferences of rural PWUD.

While rural communities across the United States share many challenges related to healthcare access and social determinants of health, there are significant regional differences that influence the experiences of rural populations. For example, rural areas in the Appalachian region often face distinct economic and infrastructural challenges compared to rural communities in the West or Midwest, including higher levels of poverty, more limited healthcare resources, and specific cultural factors impacting healthcare engagement(Driscoll et al., 2023). These regional disparities may affect recruitment, retention, and overall participation in clinical research, underscoring the importance of understanding local contexts when designing participant-centered trials.

The preferences of rural PWUD for participating in research can be determined using discrete choice experiments (DCEs). DCEs consist of a series of “choice sets,” questions which ask participants to choose between two hypothetical scenarios with unique attributes. Each choice set contains a different combination of attribute levels. Thus, participants must make tradeoffs between attributes, allowing researchers to determine the relative importance and preference of attributes. DCEs can be adapted to a variety of cultural settings and end-users. For example, DCEs have elicited preferences for HIV prevention service delivery in pharmacies among women in Western Kenya (Mugambi et al., 2024) and for HCV testing services among people using opioid substitution therapy (Radley et al., 2019). Unlike other preference elicitation methods, DCEs allow for the simultaneous assessment of multiple factors influencing choices, providing a comprehensive understanding of the relative importance of various attributes related to clinical trial participation. DCEs have not yet been applied to assess PWUD’s preferences for participating in clinical trials.

We administered a DCE to participants of the Peer-based Retention of People who Use Drugs in Rural Research (PROUD-R2) study (Young et al., 2022) at sites in rural, western Oregon and Appalachian Ohio to elicit preferences for participating in clinical trials of potential medical innovations (e.g. new treatments for substance use disorder, HIV, and HCV).

2. Methods

2.1. Ethics

All procedures were performed in compliance with relevant laws and institutional guidelines. The study received ethical approval from a central institutional review board (University of Utah IRB_00117831; May 7th, 2019). A written informed consent was obtained from all participants before study activities were conducted. Procedures, including conducting study activities in a private place, were taken to ensure participant privacy.

2.2. Study setting

This study was part of the multi-state PROUD-R2 study that tested rural peers’ ability to improve study retention (Clinicaltrial.gov identifier: NCT03885024) (Young et al., 2022). The sites in rural Appalachian Eastern Kentucky, Appalachian Southeastern Ohio, and western Oregon were specifically selected due to their high burden of opioid use and overdose, alongside the presence of established research infrastructure through the Rural Opioid Initiative (ROI) (Jenkins et al., 2022). These regions face considerable challenges related to opioid epidemics, including elevated overdose rates and limited healthcare resources. Across the three sites, the local populations were predominantly white, and an estimated 15–37% lived below the poverty line, compared to the U.S. national average of 11.5% (U.S. Census Bureau, 2022a, 2022b; Shrider and Creamer, 2023).

2.3. Development of choice tasks, attributes, and levels

The DCE was developed using standard guidelines, starting with a review of the prior literature, followed by qualitative methods to identify key design attributes and levels that were most salient in driving decision making for participating in clinical trials (Mangham et al., 2009). In-depth interviews were conducted among 35 PWUD across Kentucky (n=10), Ohio (n=10), and Oregon (n=15). In-depth interview methods and results have been described in detail elsewhere (Lancaster et al., 2025). In brief, participants were purposively recruited from the ROI study (Jenkins et al., 2022). There was an equal distribution of men and women, and the median age of participants was 39 years (IQR: 31, 46). The interview guide topics included: background, drug use and clinical care, perceptions of clinical trial research, motivations for clinical trial participation, and preferences for clinical trial participation. Interviews were digitally recorded and transcribed. Data were analyzed using content and thematic methodologies. Two coders independently applied to each transcript a common codebook that was initially based on conceptual attributes identified in the literature review. Several themes emerged around the day, times, and frequency of clinical trial visits and transportation support. We used these themes to develop the final attributes and levels for the DCE (Table 1).

Table 1.

Attributes and levels included in the discrete choice experiment following analysis of 35 in-depth interviews conducted among people who use drugs in rural Kentucky, Ohio, and Oregon.

Attributes Levels
Appointment days – Weekdays: Monday through Friday
– Weekends: Saturday and Sunday
Appointment times – Mornings: 8am to 12pm
– Afternoons: 1pm to 5pm
– Evenings: 5pm to 9pm
Transportation or location for appointment – Travel reimbursement: “You receive cash or a gift card that could be used to cover costs used to come to study office”
– Phone: “You speak with study staff by phone instead of coming to a study office”
– Driver: “A driver picks you up to come to study office”
– Public place: “You meet study staff in a public place that is convenient for you”
– Videochat: “You speak with study staff by web video, like Facetime or Skype instead of coming to a study office”
Appointment length – 1 hour
– 2 hours
– 3 hours
Visit frequency – Every week
– Every 2 weeks
– Every month
– Every 3 months
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2.4. DCE design

The DCE design was generated using a d‐efficient design created in NGENE 1.0 software following common DCE practices (Reed Johnson et al., 2013; Rose and Bliemer, n.d.). The statistically generated experimental design ensured that the parameter or utility coefficient of each level could be retrieved with the least number of choice sets presented to the participant. Based on prior work, we determined a design that included 10 or less choice cards with two scenario descriptions was feasible and cognitively appropriate (Kohler et al., 2017; Lancaster et al., 2020).

For this study, the final full DCE design included 30 choice sets divided into three blocks of 10 questions. Using these blocks of questions, each participant was randomly assigned to one block of 10 questions that were constructed for forced choice between two alternatives, “Study A” and “Study B” (Supplemental Figure 1). DCE sample size estimates are based mainly on rules of thumb, typically with a minimum sample size of 100 participants per subset (Bridges et al., 2011; Marshall et al., 2010). Following this guidance and to balance feasibility with timeliness of data collection, we included more than 100 participants per site for our DCE.

2.5. Survey administration

The procedures of the PROUD-R2 study have been described in detail elsewhere (Young et al., 2022). In brief, we leveraged ROI infrastructure to recruit PWUD at sites in Kentucky, Ohio, and Oregon for a randomized clinical trial to test the effectiveness of a peer-based retention strategy over a 12-month period. Participants were eligible if they were age 18 or older, used opioids or injected any drug to get high in the past 30 days, and resided in the study area. Prior to enrollment, participants completed informed consent with a trained study staff member. The DCE component was administered from September 2020 to July 2022 as part of the baseline survey. When enrolled participants completed the baseline survey in-person, study staff would randomly assign participants to one of the three DCE blocks with 1:1:1 ratio using REDCap; randomization was stratified by site (KY, OH, OR) with permuted random blocks. Participants completed the DCE in REDCap on a tablet or computer.

Given the stay-at-home advisories issued during the COVID-19 pandemic, the Kentucky study team shifted from in-person survey administration to phone survey administration. Administering the DCE by phone was not feasible, and therefore, most Kentucky participants did not complete the DCE and were not included in analyses.

2.6. Statistical analysis

We examined participants’ baseline sociodemographic characteristics using conventional descriptive statistics. For DCE data, subjects’ relative preferences for levels of each visit attribute were determined using conditional logit regression analysis to predict task choice (chosen versus not chosen). While mixed logit models can assess heterogeneity in preferences across individuals, we chose to use a conditional logit model because it provides straightforward estimation of average preference weights at the population level, which aligns with our study objectives. Additionally, given our sample size and focus on broader population-level preferences, the conditional logit approach was more feasible. Effects-coded models used levels of visit attributes as predictors while controlling for within-subject correlation. These models used all but one level of each attribute as covariates. Each level covariate was coded 1 (represented in choice task), 0 (absent in choice task), or −1 (omitted referent level represented in choice task). Conditional logit regression analyses were conducted for all subjects, then stratified by study site and gender. For each model, we calculated preference weights for each attribute level (for which a higher number indicates greater preference) and relative utility for each attribute (represented by the absolute difference between the highest and lowest preference weights with a higher value indicating higher utility). Preference weights indicate the strength of participants’ preferences for each attribute level, with higher numbers reflecting stronger preferences. The term ‘relative utility’ in our analysis refers to the utility range within each attribute, calculated as the difference between the highest and lowest preference weights for that attribute. Larger ranges indicate that the attribute has a greater influence on participants’ decisions. The differences between these utility values for various levels within an attribute reflect the relative importance of those levels; larger differences suggest greater influence on decision-making. Data management and analyses were performed using SAS version 9.4 (SAS Institute Inc., Cary, North Carolina).

3. Results

3.1. Sample Characteristics

Among the 478 participants included in this analysis, 71% (n=337) were from Oregon. The majority were male (63%; n=299) and nearly all were white (85%; n=404) and non-Hispanic/Latino (95%; n=451). Less than one-third had not earned their high school diploma (29%; n=136). Nearly three-quarters did not have a driver’s license (73%; n=343) and only 37% had not experienced transportation problems in the past 6 months (n=176). Over half reported unstable housing in the past 6 months (65%; n=308). However, most used the internet daily (73%; n=347) and had a smartphone with internet access (61%; n=284). Nearly all had ever injected drugs to get high (95%; n=462) and 53% selected an opioid as their drug of choice (n=250).

3.2. Clinical Trial Preferences

Across the sample, transportation mode was the most valued feature for clinical trial participation and participants consistently preferred travel reimbursement over other transportation options (preference weight=0.87; p<0.01; Figure 1; Supplemental Table 1). For example, men (relative utility=1.17) and women (relative utility=1.12) overwhelmingly preferred receiving financial compensation for travel costs over their least preferred option, videochat (Supplemental Table 2). Despite high smartphone and internet usage among participants, videochat had the lowest preference weight (preference weight=−0.29; p=0.09; Figure 1; Supplemental Table 1).

Figure 1.

Figure 1.

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Preference weights for participating in clinical trials among people who use drugs in Appalachian Ohio and western Oregon enrolled in the Peer-based Retention of People who Use Drugs in Rural Research (PROUD-R2) who completed the discrete choice experiment between September 2020 and July 2022. Preference weights were calculated from discrete choice experiment data using a conditional logit model.

Participants strongly favored shorter visits, with a 1-hour visit having 0.44 greater utility compared to a 3-hour visit (preference weight of 1-hour visit=0.25; p<.01; Figure 1). Importance of time of day was also evident, as mornings were significantly less preferred (preference weight=−0.16; p<0.01; Figure 1). Appointment frequency (weekly, biweekly, monthly) and day of week had minimal impact on decision-making.

Women and men across sites placed similar relative utility on each of the study attributes and ranked attribute levels similarly (Supplemental Table 2). However, there were some differences by site. Although both Oregon and Ohio participants placed the highest relative utility on transportation reimbursement compared to the least preferred transportation option (i.e., virtual participation via videochat for Oregon and a driver for Ohio), this attribute appears to be more important to Oregon (relative utility=1.59) participants than Ohio participants (relative utility=0.62; Supplemental Table 3). Additionally, participants in Ohio showed a slight, non-significant preference for videochat (preference weight = 0.04, p =0.91), while participants in Oregon were significantly less likely to prefer this mode (preference weight = −0.51, p =0.03).

4. Discussion

Among PWUD in rural, western Oregon and Appalachian Ohio, transportation support, particularly travel reimbursement, was the most valued factor influencing participation in research and healthcare. In contrast, videochat was the least preferred option, despite high smartphone and internet usage. Participants also strongly favored shorter visits (1 hour) over longer ones and preferred afternoon or evening appointments over mornings. Visit frequency (weekly, biweekly, or monthly) had minimal impact on decision-making, suggesting that logistical barriers such as travel and time commitment play a larger role in determining participation. These findings emphasize the need to prioritize financial assistance for transportation, minimize participant burden, and offer flexible scheduling to enhance engagement in rural research and healthcare.

Transportation barriers have long been recognized as a significant challenge for rural populations, limiting access to both healthcare and research participation (Batista et al., 2016; Davis et al., 2019; Douthit et al., 2015; Friedman et al., 2015; Hetrick et al., 2021). The strong preference for mileage reimbursement may reflect not only a desire to offset travel-related burdens but also broader financial vulnerabilities. However, for participants who lack a driver’s license, are unhoused, or face daily injection drug use, mileage reimbursement alone may not address their transportation needs and barriers effectively. Many may lack access to personal vehicles, making reimbursement insufficient without additional supports such as shuttle services or mobile clinics. PWUD in rural settings have previously highlighted financial compensation as a primary motivator for study participation, underscoring the importance of accounting for economic vulnerability when designing equitable research protocols (Hetrick et al., 2021). While other transportation options, such as phone participation, driver services, and meeting in public locations, were less valued, they may still be viable alternatives for some participants. Videochat was the least preferred option despite growing reliance on telehealth (Fleddermann et al., 2025; Pham et al., 2023). This suggests in-person participation in a private setting may be perceived as more familiar, trustworthy, or accessible. While digital solutions can expand access, it is important to consider trust, digital literacy, and personal preferences in rural settings (Lin et al., 2024; Wyte-Lake et al., 2024).

Minimizing the burden by reducing visit length was another critical factor for participants, with strong preferences for 1-hour visits over longer ones. The negative preference weights of 2–3-hour visits reinforce that minimizing time burden is essential for retention. This aligns with broader evidence showing that longer study visits are barriers to enrollment and retention, particularly among individuals facing employment constraints or other competing demands (Friedman et al., 2015; Hetrick et al., 2021; Hilliard et al., 2023; Natale et al., 2021). Shorter visits may improve feasibility and increase engagement and retention over time (Wong et al., 2021). Time of day also influenced participation, with mornings being the least preferred option. This is consistent with the schedules of PWUD who may be managing transportation limitations, competing priorities, and sleep disruptions (Hetrick et al., 2021; Hilliard et al., 2023; Schneider et al., 2024).

Although overall trends were consistent, site-specific variations highlight the need for tailored approaches. For instance, participants in Ohio showed a slight, though not statistically significant, preference for videochat, whereas those in Oregon were significantly less likely to prefer this mode. While telehealth has expanded nationally in recent years, uptake remains uneven across rural areas (Hammerslag et al., 2023). These findings emphasize the importance of designing research and healthcare interventions that are both socially and regionally responsive. It is important to note that the DCE presents hypothetical scenarios; actual participation behaviors may differ; however, evidence does suggest these choices may predict real-world behavior (Salampessy et al., 2015). Additionally, the sample was predominantly white and non-Hispanic, which may limit its generalizability to more diverse rural populations. Due to the scope of our analysis, we focused on estimating and interpreting preference weights and utility ranges for individual attributes and did not calculate attribute-level relative importance scores or perform formal tradeoff or willingness-to-pay analyses. Future research should explore how preferences evolve over time and across different types of studies (e.g., medication trials versus behavioral interventions). Mixed-methods approaches, incorporating qualitative interviews, could provide deeper insights into the motivations, barriers, and concerns of PWUD regarding research and healthcare participation.

Ultimately, rural PWUD emphasized the need for transportation support, shorter appointments, and flexible scheduling to make research and healthcare participation more accessible. Providing financial assistance for travel, reducing time commitments, and offering convenient appointment options may help reduce barriers and foster greater participation, trust, and engagement. Centering the preferences and experiences of PWUD in rural settings is essential to ensuring equitable access to vital health resources and research opportunities, ultimately strengthening well-being and health outcomes in these communities.

Supplementary Material

Supplement

Table 2.

Baseline characteristics of participants (n=478) in Appalachian Ohio and western Oregon enrolled in the Peer-based Retention of People who Use Drugs in Rural Research (PROUD-R2) who completed the discrete choice experiment between September 2020 and July 2022.*

Characteristic
Site
 Ohio 141 (29.5)
 Oregon 337 (70.5)
Age, years, mean (SD) † 39.7 (10)
Gender
 Male 299 (62.6)
 Female 177 (37)
 Transgender 2 (0.4)
Race
 White 404 (84.5)
 African American/Black 7 (1.5)
 American Indian/Alaskan Native 25 (5.2)
 Asian/Pacific Islander/Native Hawaiian 2 (0.4)
 Mixed race 28 (5.9)
 Other 12 (2.5)
Hispanic/Latino
 Yes 24 (5.1)
 No 451 (94.9)
Education
 < High school 136 (28.5)
 High school diploma/GED 180 (37.7)
 At least some college 162 (33.9)
Currently have driver’s license
 Yes 130 (27.5)
 No 343 (72.5)
Transportation problems in last 6 months
 Never 176 (37.1)
 1–4 times in past 6 months 99 (20.9)
 At least once a month 126 (26.6)
 Almost every day 73 (15.4)
Homeless in past 6 months
 Yes 308 (64.8)
 No 167 (35.2)
Frequency of internet use
 Never 39 (8.2)
 Several times a month-Several times a week 90 (18.9)
 About once a day or more 347 (72.9)
Own a smart phone with internet access
 Yes 284 (60.6)
 No 185 (39.4)
Ever injected drugs to get high 455 (95.2)
Frequency injected any drug in last 30 days ‡
 Daily or more 272 (66.8)
 Weekly or more 70 (17.2)
 Monthly or more 23 (5.7)
 Never 42 (10.3)
Drug of choice
 Opioids§ 250 (52.6)
 Stimulants 210 (44.2)
 Other# 15 (3.2)
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*

Presented as n (%) unless otherwise indicated. Frequencies may not sum to total due to missing values.

†

Age missing one value.

‡

Average frequency reported among those who reported ever injecting

§

Includes heroin, street fentanyl/carfentanil powder, opiate painkillers, buprenorphine, methadone

Includes cocaine/crack and methamphetamine/amphetamine

#

Includes prescription anxiety drugs, gabapentin, alcohol, and marijuana

Highlights.

  • Transportation support was a key factor for rural PWUD in clinical trial decisions

  • Rural PWUD valued travel reimbursement most for trial participation

  • Shorter appointments and evening time slots were strongly preferred

  • Some preferences varied by region

  • Findings support tailoring rural research logistics to participant needs

Acknowledgements

Thank you to Lisa Maybrier, Rhonda Gilliam, Renee McDowell, Cathy Neal, and Anyssa Wright for leading recruitment and data collection; and Edward Freeman for data management.

Funding sources

This work was supported by the National Institutes of Health NCATS [U01TR002631] and NIDA [UH3DA044831, UH3DA044798, UG1DA015815]. KEL was supported by NIDA through K01DA048174. ATE was supported by NIDA through F31DA054752. Funding sources were not involved in the study design, collection, analysis and interpretation of data, writing of the report, nor the decision to submit the article for publication.

Data statement

De-identified data will be made available upon written request to Principal Investigator, P. Todd Korthuis.

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Associated Data

This section collects any data citations, data availability statements, or supplementary materials included in this article.

Supplementary Materials

Supplement
NIHMS2130875-supplement-Supplement.pdf (164.5KB, pdf)

Data Availability Statement

De-identified data will be made available upon written request to Principal Investigator, P. Todd Korthuis.

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