Digital Enrollment and Survey Response of Diverse Kidney Transplant Seekers in a Remote Trial (KidneyTIME): An Observational Study

Abstract

Introduction: The feasibility of enrolling and retaining diverse kidney transplant (KT) seekers in remote studies is sparsely reported. Aims: This study examined the use of a mobile communication strategy to enroll and retain participants within a clinical trial of an automated digital intervention to promote self-learning for kidney transplant access. Materials and Methods: Adult KT-seekers were identified from an administrative database at a transplant center and recruited by email or text supplemented by verbal prompts. Multivariable logistic regression was used to explore participant- and study-level characteristics associated with enrollment and response rates. Results: Between April 2022 and June 2023, 743 patients were invited to participate, and 422 were enrolled. Enrollers were more likely to be younger (aOR 1.02; p < 0.001). Early enrollment was associated with text message invitation (OR 2.69, p ≤ 0.014). Survey completion at 1 month was similar across patient sociodemographic, clinical, and study characteristics; however, participants self-reporting Black race were underrepresented at month 6 (OR 0.55, p = 0.015) and month 12 (aOR 0.37, p = 0008), and males were underrepresented at month 12 (aOR 0.45, p = 0.028). Conclusion: Mobile communication methods are viable for enrolling diverse KT-seeking patients and collecting survey data remotely. More work is needed to learn how to best recruit older people and retain diverse groups long-term.

1. Introduction

Living donor kidney transplantation (LDKT) is the gold standard treatment for kidney failure, yet few receive one [1]. Providing education to patients and their social network is effective in increasing LDKT access; however, the reach of this education has been limited using traditional approaches. Digital interventions delivered remotely could improve access to LDKT education [2]. The number of remote clinical trials for disseminating LDKT education is surprisingly low. One possible reason is the challenge of remotely enrolling and collecting patient reported outcomes among individuals with kidney failure, who might not have access or familiarity with devices [3,4] and may also experience barriers of fatigue, vision, and cognition related to medical conditions [5,6]. This study examined the use of a mobile communication strategy to enroll and retain participants within a clinical trial of an automated digital intervention to promote self-learning for kidney transplant access.

Electronic messaging and web surveys have been widely used to enroll and collect data for clinical trials in recent times [7,8]. The advantages of being low cost and efficient [9] and providing access to hard-to-reach populations make this method appealing for research [10,11]. There is also evidence that data collected via mobile phones and web surveys are feasible, reliable, and valid [12,13]. For example, the use of text messaging for data collection in primary care research has shown high response rates [14]. Little is known about the use of electronic messaging in combination with web surveys to enroll participants and collect patient-reported outcomes among people seeking kidney transplants (KT) [15,16] and whether there are demographic or clinical characteristics that identify individuals at risk of lower enrollment and survey response (computer ownership and dialysis duration). Prior studies of chronic kidney disease populations have observed the “digital divide” to be present including the uneven distribution in the access to and use of technology by socioeconomic status, age, and racial minority status [17,18,19,20]. Given the value of remote studies, enriching our understanding of and overcoming selection biases and inequities arising from the digital environment is important.

In April 2022, KidneyTIME, a primarily remote longitudinal randomized controlled trial, was launched to test the effect of a smartphone-optimized digital intervention on LDKT determinants and behaviors of KT-seekers. The results of the trial are pending publication. In this study, we report on the approach, implementation, and process outcomes of remotely enrolling KT-seekers and collecting survey data. The primary aim of this study is to investigate participant characteristics associated with enrollment and survey response rates. As previous research has suggested, participant characteristics such as age, sex, and socioeconomic status can significantly influence response rates [9,10,21,22,23]. We also aim to investigate whether these factors influenced response rates by communication type (email, text).

2. Materials and Methods

2.1. Study Design

This is a study of enrollment and retention data from a randomized control trial at a single transplant center that evaluated the effect of the KidneyTIME behavioral intervention for LDKT access. The protocol for the KidneyTIME trial is available at ClinicalTrials.gov (NCT05154773) published on 16 November 2021. The intervention included viewing and sharing web-based videos and email or text messages to use the site at 3-week intervals. Active participants received 4 electronic surveys over 12 months. The study design, procedures, and informed consent were approved by the University at Buffalo institutional review board, a research ethics committee that reviews and approves human subjects’ research [24].

2.2. Participants

Participants were recruited between April 2022 and July 2023 from a transplant program in Buffalo, New York. Eligible patients were identified using the hospital’s electronic medical records. Only patients referred to the transplant center for a kidney transplant, who were English speaking and adult age (18+ years), and provided an email address or text number were eligible to participate. Exclusions were previous exposure to the intervention and absence of internet access.

2.3. Study Implementation Process

Figure 1 outlines the study enrollment and survey process which is informed by Dillman et al.’s influential Tailored Design Method utilizing insights from social exchange theory to increase respondent desire to reciprocate based on advance notice of the coming survey, clear instructions, reminders, and incentives [25,26]. Below, we describe recruitment, enrollment, data systems, and design of information, including study messages and surveys.

Recruitment—Eligible patients were informed about the study by clinical staff during telephone conversations to schedule a clinic appointment for a transplant evaluation (pre-evaluation) or were informed by study staff via opt out email and mailed letter if previously evaluated for a kidney transplant (post-evaluation). Eligible patients received an invitation email or text message based on the patients’ preferred modality as indicated through telephone conversation or in the medical record. Email was chosen if no preference was indicated.

Enrollment and survey completion—All email and text messages contained a unique web link that the user opens in their web browser. The enrollment web link opened to a survey that combined consent, baseline questions (24 questions), an embedded 13 min study video, and post-video questions (29 questions). Completion of the first web survey triggered follow-up surveys sent at 1, 6 and 12 months (46 questions each). If any survey had not been completed, automated reminder messages were sent every 3 days for a total of 2 enrollment reminders and 5 survey reminders. To maximize the response rate, participants who did not respond to the initial message were contacted verbally by telephone or in the clinic by the researcher to help them troubleshoot any technology barriers and prompt completion until 4–6 months passed for enrollment or 1 month passed for follow-up survey completion. Call attempts were made at varying times of the day and days of the week to reach each non-responder. In these calls, non-responding individuals were offered push notifications of the survey. Participants were not offered the option to provide their survey responses via phone or mail. The research staff followed a script to conduct the telephone calls. Researchers documented contact attempts and outcomes in a study tracking database regarding reasons for non-enrollment and non-completion (when offered), solutions offered, and outcomes. The surveys were not anonymous, and patients did receive financial remuneration of USD 25 per survey completed (Total USD 100). Participants did not receive surveys if they reached study endpoints, including receipt of a kidney transplant or determination of transplant ineligibility by the transplant center.

Several opt-out approaches were available. Patients who did not want to be invited to participate in the study could inform the staff at the time of verbal invitation. Potential participants could also ignore the email or text message invitations and could opt out by calling the researchers, by emailing the researchers, or by replying STOP to the text or ‘unsubscribe’ to the email, and would no longer receive invitations or be contacted.

Data Systems—Online consent, randomization, intervention delivery, longitudinal data collection, and intervention use tracking were supported by Alchemer^® (Louisville, CO, USA), an online survey platform built on the Amazon Web Services (AWS) cloud and maintained by RK Software Inc. (Long Island City, NY, USA) to configure and operate direct-to-participant studies. Alchemer provides Application Programming Interfaces (APIs) and user interfaces for participants, the study team, and logistical partners, utilizes 3rd party services such as Twilio to distribute SMS communications to participants, and abides by all HIPAA security and breach rules. Data from Alchemer were imported into a SQL Server database to create a custom dashboard to display study milestone completion data and survey data, which were stored in a secure, web-based software platform (AWS Cloud) designed to support data capture for research studies. Protective measures (e.g., encryption) were carried out to safeguard all study data. The study did not require patients to download any software, install a separate app, or enter login information.

Design of information—The information provided to patients in the messages, scripts, and surveys, and the steps in the process were designed from the participant’s point of view with input from potential participants and a literacy expert to reduce cognitive demand. All messages used simple English language, provided the most important information first, and set a clear action. All messages (except the consent) did not require scrolling to read. To simplify the online response process, we utilized unique web links, and a URL shortened to create a simplified location to click and directly access electronic consent and surveys without requiring logging in.

Messages requesting enrollment were personalized by using the patient’s first name and designed to provide sufficient initial information to increase patients’ engagement and therefore had a reduced level of detail that outlined what would be involved, whereas the consent document contained standard institutional wording. The sender of the email messages was “Buffalo Transplant” to illuminate a topic important to participants and make it easily searchable and identifiable in spam folders. As shown in Figure 2, the enrollment invitation additionally included the hospital acronym in the subject line (ECMC) and introduced the primary investigator by name, clinical title (email only) and a photo (email only). Messages highlighted the low difficulty of study tasks (watching some videos, answering short simple questions), use of any device (phone, computer), convenient participation (“whenever you want”), compensation for their time, and made appeals both to helping the researchers and receiving novel information. Similarly, messages for survey completion reinforced importance of and appreciation for their contribution, minimum time to do study tasks, and compensation for their time.

To attract and retain the highest number of participants, all surveys were adapted for readability and suitability for smartphone viewing in an iterative fashion [27] and built using solely radio elements, single question per page, and automatic advancement of single-answer questions. The questions in the follow-up surveys related to self-report outcomes are provided in

Table 1. Living donor kidney transplant (LDKT) questions in follow-up surveys related to self-report outcomes.

LDKT Knowledge: Living kidney donors must go on a special diet for the rest of their lives. Living kidney donors will have to take medications for the rest of their lives. Kidneys from living donors last longer than kidneys from deceased donors. A living donor who needs a kidney transplant later in life is given priority for transplantation. Even if your living donor doesn’t match you, they can still help you get a transplant by being part of a kidney exchange. A living donor must use their insurance or pay out of pocket for the donation surgery. A living donor can decide not to donate at any time. Living donors must be under 50 years old. Female living donors have trouble getting pregnant. Most living donors will develop kidney disease later in life. Living donors have a shorter life expectancy because of donation. There is a program that will pay for some of the living donor’s costs (travel, meals, and lodging).

LDKT Concerns: I am concerned that the costs of donating a kidney would be too high. I am concerned that the evaluation process to be a living donor is too complicated. I am concerned that the living donor will not be able to stay healthy with only one kidney. I am concerned that something bad could happen to the living donor. I am concerned people in my life may not be healthy enough to donate a kidney to me. There’s no point in finding a living kidney donor because I can get a kidney from someone who has passed away.

LDKT Readiness: I’m not thinking about living donors. I’m beginning to think about living donors. I’m seriously considering living donors. I have someone willing to be evaluated as a possible donor. I have someone who is approved to be a donor.

. The full survey is available upon contacting the corresponding author. A progress bar was provided, and messages reinforced that their answers were saved and that they had the ability to return to the survey at any time using their unique link.

2.4. Predictors and Outcomes

In this study, we investigated enrollment and survey response rates as outcomes. Enrollment rates were measured as the proportion of eligible patients who signed the electronic consent to participate. We assessed whether enrollment occurred early, defined as consent signed prior to any reminder message being sent and reminder call being made. Participant responses to follow-up surveys at months 1, 6, and 12 were classified as ‘yes’ survey completed or ‘no’ survey not completed among patients remaining in the study. Web survey breakoffs (participants who started the survey but did not complete it) were not counted as responses. We used a sample of available call log notes to determine reasons for non-enrollment and survey non-completion.

We extracted the patient-level predictors from the hospitals’ electronic medical records including sociodemographic (age, sex, race, zip code) and health (prior kidney transplant, estimated post-transplant survival (EPTS), requiring chronic dialysis) characteristics. These data were supplemented by self-reported information from study surveys. EPTS was used to classify the health level of participants. The EPTS is based on a range of 0–100% with higher EPTS scores predicting worse patient survival after kidney transplantation. The Area Deprivation Index (ADI) was used to classify patient socioeconomic status based on their ZIP code of residence [28,29]. The ADI is reported from 0 to 100%, with the highest percent designating areas with greatest social economic disadvantage. Health literacy was measured with two items [30], scored on 4-point Likert-type scales: “How often do you have someone help you read hospital materials?” “How confident are you filling out forms by yourself?” (Cronbach’s alpha = 0.62 in the trial). Basic social support was measured by a 6-item survey [31], with items scored on 4-point Likert-type scales (4 options): Frequency someone is available to: prepare meals if unable to do so [instrumental], talk to doctor if needed [instrumental], help with daily chores if sick [instrumental], give good advice about crisis [emotional], confide or talk to about problems [emotional], who can understand your problems [emotional]. (Cronbach’s alpha = 0.90 in the trial).

2.5. Data Analysis

Descriptive statistics are presented for the trial sample as frequency count with column proportion, mean and standard deviation if normally distributed, or median and 25th/75th percentile if non-normal. Logistic regression was used to evaluate whether sociodemographic characteristics, health factors, invitation method (email or text method), and device used (phone, not phone) were predictors of whether a participant responded. We first performed univariate analysis (unadjusted) for each variable, and then performed a multivariate analysis (adjusted) that included all variables with differences at p < 0.05. The results are summarized as unadjusted odds ratio (OR) or adjusted odds ratios (aOR) with 95% confidence intervals (95%CI). In sensitivity analysis, we checked for effect modification by electronic modality (email/sms) including an interaction term for each participant’s characteristic by electronic modality in the models of our key outcomes, and there was no evidence of electronic modality by baseline characteristic interaction. All data were analyzed using SAS software, version 9.4 (SAS Institute., Cary, NC, USA) and SPSS Statistics, version 25.0 (IBM Corp, Armonk, NY, USA). A p-value of <0.05 was considered statistical significance.

Qualitative content analysis was performed by a qualitative researcher (M.K.) who coded the field notes (n = 418) using an excel spreadsheet based on two a priori topics delimited by two study objectives: (1) reasons why participants declined participation and (2) reasons why patients did not complete surveys. Another researcher, a transplant surgeon and study primary investigator (L.K.) reviewed the codes for possible content misalignment.

3. Results

Figure 3 shows the flow of recruited patients from assessment for eligibility to survey completion. Data were collected between April 2022 and August 2024. A total of 743 eligible patients in the pre-transplant process were invited to participate, and 422 enrolled (57%). Trial participants’ mean age was 54 ± 14 years, 52% were non-Hispanic white, 36% African American, 57% male, and 44% had a total household income ≤ USD 30,000 (

Table 2. Baseline characteristics of trial sample.

Characteristics	Trial Sample (n = 422)
Pre-evaluation phase at study entry	51% (214)
Post-evaluation phase at study entry	49% (208)
Enrolled using email	65% (274)
Enrolled using text	35% (148)
Completed baseline survey with phone	67% (263)
Age, years mean ± SD	54 ± 14
Sex, Male	57% (241)
Black or African American	36% (151)
non-Hispanic White	52% (220)
Hispanic or Latino	5% (21)
Other race or ethnicity	7% (30)
Education, less than college degree	61% (256)
Single adult household	27% (114)
Prior kidney transplant	12% (51)
Chronic dialysis, none	25% (104)
Chronic dialysis < 1 year	31% (130)
Chronic dialysis 1–3 years	25% (106)
Chronic dialysis > 3 years	19% (82)
EPTS a, continuous median (25th, 75th percentile)	42 (21, 68)
Medicaid, State or VA insurance	51% (213)
Employed	22% (93)
Total annual household income ≤ $30,000	44% (187)
Area Deprivation Index, mean ± SD	74 ± 23
Number of close friends or relatives None	2% (10)
Number of close friends or relatives 1–3	46% (193)
Number of close friends or relatives 4+	52% (216)
Has working computer	68% (285)
Has working internet-capable cell phone	95% (396)
Sends or receives text messages	97% (408)
Uses email	91% (380)
Watches videos online	89% (372)
Uses social media	79% (331)
Has active Facebook account.	77% (321)
Health Literacy median (25th, 75th percentile)	7 (6, 8)
Social Supportc median (25th, 75th percentile)	20 (15, 23)

SD, standard deviation. ^a EPTS, Estimated Post-transplant Survival. Computed using https://optn.transplant.hrsa.gov/data/allocation-calculators/epts-calculator/ (accessed on 1 August 2023).

). The majority enrolled through email (65%) rather than text and completed the baseline survey with their phone (67%). All baseline survey completers entered the study to receive timeline-driven surveys across a 12-month intervention period. Follow-up surveys were not sent to patients who met study endpoints. Therefore, survey response rates were 61% (228/376) at month 1, 66% (205/313) at month 6, and 62% (108/175) at month 12 (Figure 3).

3.1. Enrollment

Table 3. Enrollment rate by characteristics of study participants *.

Characteristics	Enrolled
	Crude Model		Adjusted Model
	OR (95% CI)	p Value	aOR (95% CI)	p Value
Pre-evaluation phase (post-evaluation)	1.03 (0.06, 16.56)	0.984	-	-
Enrolled using text (email)	0.75 (0.55, 1.02)	0.069	-	-
Age, decreasing by year	1.02 (1.01, 1.03)	<0.001	1.02 (1.01, 1.03)	<0.001
Sex, Male (Female)	0.74 (0.56, 0.97)	0.029	0.77 (0.58, 1.01)	0.060
Race, Black or African American (other)	0.76 (0.57, 1.01)	0.054	-	-
Prior kidney transplant	1.01 (0.62, 1.65)	0.960	-	-
Dialysis duration < 1 year (none)	0.85 (0.65, 1.12)	0.248	-	-
Dialysis duration 1–3 years (none)	1.58 (0.44, 5.64)	0.481
Dialysis duration > 3 years (none)	1.15 (0.88, 1.50)	0.318
Estimated post-transplant survival > median	0.77 (0.59, 1.01)	0.065	-	-
Medicaid, State or VA insurance	0.94 (0.71, 1.24)	0.643	-	-
Area deprivation index, increasing	1.00 (1.00, 1.01)	0.820	-	-

OR, Odds Ratio; 95% CI, Confidence Interval. * Bold indicates significance of p values.

provides the association of enrollment rate by participant and study characteristics. In the adjusted model, enrollers were significantly more likely to be younger (aOR 1.02; p < 0.001). Enrollment rate did not significantly differ by other sociodemographic or health characteristics or electronic modality.

3.2. Early Enrollment

Table 4. Early consent by characteristics of study participants *.

Characteristics	Early Consent
	OR (95% CI)	p Value
Pre-evaluation phase (post-evaluation)	2.29 (0.82, 6.46)	0.116
Enrolled using text (email)	2.69 (1.22, 5.94)	0.014
Completed baseline survey with phone (not phone)	1.27 (0.52, 3.13)	0.602
Age, decreasing by year	0.98 (0.94, 1.01)	0.179
Sex, Male (Female)	1.25 (0.56, 2.80)	0.590
Race, Black or African American (other)	1.29 (0.58, 2.87)	0.527
Education, less than college degree (college)	0.64 (0.29, 1.41)	0.265
Single adult household	0.56 (0.25, 0.80)	0.170
Prior kidney transplant	2.55 (0.90, 7.20)	0.078
Dialysis duration < 1 year (none)	0.82 (0.33, 1.99)	0.653
Dialysis duration 1–3 years (none)	0.89 (0.35, 2.27)	0.799
Dialysis duration > 3 years (none)	1.96 (0.82, 4.68)	0.130
Estimated post-transplant survival (EPTS) > median	2.34 (0.98, 5.56)	0.054
Medicaid, State or Veterans insurance	1.72 (0.77, 3.85)	0.189
Employment status
Retired (full or part-time job)	1.37 (0.43, 4.34)	0.591
Disability (full or part-time job)	1.13 (0.38, 3.36)	0.824
Unemployed (full or part-time job)	1.39 (0.25, 7.56)	0.707
Total annual household income ≤ $30,000	1.46 (0.64, 3.33)	0.373
Number of close friends or relatives < 4 (4+)	1.08 (0.49, 2.40)	0.842
Has working computer	1.05 (0.45, 2.48)	0.911
Watches videos online	3.29 (0.44, 24.83)	0.249
Uses social media	1.11 (0.41, 3.04)	0.834
Has active Facebook account	1.02 (0.40, 2.62)	0.962
Low Health Literacy (<25th percentile)	0.55 (0.19, 1.64)	0.284
Low Basic Social Support (<25th percentile)	0.59 (0.20, 1.77)	0.346

OR, Odds Ratio; 95% CI, Confidence Interval. * Bold indicates significance of p values.

provides associations of early consent rate by participant characteristic. Of 422 who enrolled, 37% signed the electronic consent early, prior to receiving a reminder message and after a single electronic study invitation, indicating a faster response time. Early enrollment was significantly more likely among participants receiving text links (OR 2.69, p = 0.014).

3.3. Survey Response

Table 5. Survey completion at 1, 6, and 12 months by characteristics of study participants *.

Characteristics	Survey Completed Month 1		Survey Completed Month 6		Survey Completed Month 12
	OR (95% CI)	p Value	OR (95% CI)	p Value	Crude OR (95% CI)	p Value	Adjusted aOR (95% CI)	p Value
Intervention (control)	0.70 (0.46, 1.06)	0.090	1.18 (0.74, 1.88)	0.491	0.99 (0.54, 1.81)	0.960	-	-
Pre-evaluation phase (post-evaluation)	0.83 (0.55, 1.25)	0.370	0.96 (0.60, 1.54)	0.870	0.99 (0.54, 1.83)	0.987	-	-
Enrolled using text (email)	0.66 (0.42, 1.01)	0.056	0.98 (0.60, 1.59)	0.930	0.49 (0.26, 0.92)	0.027	0.55 (0.27, 1.13)	0.104
Age, decreasing by year	1.00 (0.99, 1.02)	0.666	0.99 (0.98, 1.01)	0.333	0.98 (0.95, 0.99)	0.036	0.99 (0.97, 1.02)	0.678
Sex, Male (Female)	0.95 (0.62, 1.44)	0.799	0.78 (0.49, 1.25)	0.306	0.50 (0.27, 0.95)	0.033	0.45 (0.22, 0.92)	0.028
Black or African American (other)	0.68 (0.44, 1.04)	0.076	0.55 (0.34, 0.89)	0.015	0.36 (0.19, 0.68)	0.002	0.37 (0.18, 0.78)	0.008
Less than college degree (college)	0.72 (0.47, 1.11)	0.138	0.92 (0.57, 1.49)	0.736	0.70 (0.36, 1.33)	0.275	-	-
Single adult household	0.73 (0.46, 1.16)	0.179	0.76 (0.46, 1.27)	0.291	0.82 (0.42, 1.62)	0.572	-	-
Prior kidney transplant	1.08 (0.57, 2.04)	0.822	0.77 (0.35, 1.68)	0.509	1.73 (0.71, 4.25)	0.231	-	-
Dialysis duration <1 year (none) Dialysis duration 1–3 years (none) Dialysis duration >3 years (none)	1.12 (0.65, 1.95) 1.34 (0.74, 2.43) 1.04 (0.56, 1.94)	0.681 0.332 0.896	1.27 (0.68, 2.38) 0.99 (0.51, 1.92) 0.70 (0.35, 1.38)	0.449 0.979 0.301	0.64 (0.28, 1.44) 0.80 (0.32, 2.03) 0.30 (0.12, 0.75)	0.279 0.638 0.010	0.88 (0.35, 2.21) 1.40 (0.49, 4.05) 0.46 (0.16, 1.34)	0.778 0.532 0.154
EPTS, increasing	1.00 (0.99, 1.01)	0.997	1.01 (1.00, 1.02)	0.162	1.01 (0.99, 1.02)	0.111	-	-
Medicaid, State or Veterans insurance	1.04 (0.69, 1.58)	0.838	0.81 (0.51, 1.30)	0.383	0.37 (0.19, 0.71)	0.003	0.52 (0.23, 1.19)	0.120
Employment status Retired (full or part-time job) Disability (full or part-time job) Unemployed (full or part-time job)	0.96 (0.53, 1.75) 0.90 (0.53, 1.55) 0.65 (0.26, 1.59)	0.902 0.713 0.342	0.68 (0.34, 1.35) 0.87 (0.46, 1.66) 0.41 (0.16, 1.08)	0.264 0.679 0.072	2.31 (0.89, 5.99) 0.75 (0.34, 1.65) 0.45 (0.12, 1.67)	0.084 0.473 0.230	-	-
Total annual household income ≤ $30,000	0.71 (0.47, 1.08)	0.112	0.90 (0.56, 1.43)	0.652	0.39 (0.21, 0.73)	0.003	0.70 (0.31, 1.59)	0.397
Number close friends or relatives < 4 (4+)	1.05 (0.69, 1.59)	0.822	0.78 (0.49, 1.25)	0.299	0.52 (0.28, 0.96)	0.036	0.60 (0.29, 1.22)	0.155
Has working computer	1.33 (0.85, 2.07)	0.207	1.49 (0.92, 2.43)	0.107	1.30 (0.68, 2.50)	0.434	-	-
Watches videos online	1.72 (0.90, 3.30)	0.104	1.34 (0.67, 2.71)	0.412	1.70 (0.60, 4.75)	0.316	-	-
Uses social media	1.23 (0.74, 2.06)	0.418	1.34 (0.75, 2.39)	0.320	2.11 (0.93, 4.77)	0.073	-	-
Has active Facebook account	1.30 (0.80, 2.11)	0.298	1.72 (0.99, 3.01)	0.056	1.32 (0.61, 2.87)	0.483	-	-
Low Health Literacy <25th percentile	0.72 (0.44, 1.17)	0.182	1.07 (0.62, 1.86)	0.807	0.53 (0.26, 1.07)	0.075	-	-
Low Basic Social Support <25th percentile	1.06 (0.66, 1.71)	0.808	0.95 (0.55, 1.61)	0.839	1.45 (0.69, 3.06)	0.324	-	-

OR, Odds Ratio; 95% CI, Confidence Interval. * Bold indicates significance of p values.

shows the characteristics of study participants and survey response rates overall and up to 12 months. Survey responders of the month 1 survey did not significantly differ by sociodemographic characteristics, health related factors, or electronic modality. In later surveys, individuals who were significantly underrepresented on univariable analysis self-identified as male (month 12), Black or African American race (month 6 and 12), were younger (month 12), were receiving Medicaid, state, or veterans insurance (month 12), reported a total annual household income < USD 30,000 (month 12), had fewer than four close friends (month 12), and were on dialysis longer (month 12). After multivariable adjustment, only Black or African American race (aOR 0.37, p = 0.008) and male sex (aOR 0.45, p = 0.028) were independently associated with survey completion at month 12.

3.4. Enrollment Barriers and Facilitators Uncovered

Of 88 patients who refused participation and provided comments (Figure 3), the most common barriers to non-enrollment were lack of time (n = 38) and lack of interest in the study (n = 22). Other barriers include impaired vision or mentation (n = 10), acute medical issues (n = 10), low technology skill (n = 5), high level of prior knowledge (n = 5), and unknown (n = 8).

3.5. Survey Response Barriers and Facilitators Uncovered

A total of 259 participants (66%) were called at least once to offer assistance with survey completion, and 63 provided sufficient context for analysis. The main barriers to survey completion were unawareness of the electronic notification (in their email or text messages) or email message being sent to spam (n = 33). Barriers to survey completion included lack of time (n = 22), lack of technology skill (n = 5), and medical obligations (n = 3). Facilitators were resending the survey message (push notification) and verbal instruction on how to find the message by searching “Buffalo transplant” or by searching the PI name among SMS users.

4. Discussion

Enrollment and retention are known challenges in clinical trials [32]. Historically, the process has involved in-person contact with potential participants at clinical sites and surveys by mail, telephone, or in person. Recent advances and digital technologies—including communications—have enabled clinical trials, in which electronic consent, enrollment, and monitoring are handled remotely. Conducting these processes remotely has been shown to expand the pool of participants, offering flexibility and convenience, allowing people to participate who were previously unable to, while simultaneously reducing costs [9]. However, the feasibility of such methods among people with kidney failure has infrequently been reported [33]. The KidneyTIME study was designed as a primarily remote trial connected to a hospital system in that the phone calls to potential participants were from the hospital. The KidneyTIME model used a digital infrastructure to obtain consent, randomize, deliver the intervention, and send surveys rather than using a traditional site visit model. Our model highlighted the role of delivering survey links via email or text message for enrollment and survey completion, utilizing links that do not require logging in, verbal follow-up with patients and offering to resend the links to position the action at the top of their messages, and postpaid incentives. We found that these methods were viable for remotely enrolling diverse KT-seeking patients and collecting short-term survey data except for lower recruitment of older persons and lower long-term retention of diverse groups.

In our sample of pre-KT patients, half of whom were in the pre-evaluation phase at study entry (had not yet presented for evaluation at the transplant center), a high proportion (86%) were considered eligible. By deploying fully remote on-boarding to the intervention without requiring scheduling or a physical visit, we had hoped to gain enrollment with minimal burden to patients and 57% enrolled. A comparison of our enrollment rate to other studies is difficult since all prior effectiveness trials of LDKT access interventions required physical visits to initiate or fully participate in the intervention [34,35,36,37,38,39,40,41,42]. One feasibility RCT of a fully remote LDKT access intervention mailed participants the intervention tablet and reported a 60% enrollment rate among waitlisted patients [43]. Despite alleviating structural barriers by allowing flexibility based on personal availability, most non-participants of our trial reported feeling busy or not interested in research participation. Other constraints were medical obligations, impaired vision or mentation, and technology issues. These concerns may be exacerbated by remote models indicating that alternative pathways for enrollment are needed [9].

The results are promising for engaging diverse patients to enroll in the study. There was broad representation of gender, lower literacy, and black race minority groups and participants were predominantly low-income with high levels of Medicaid insurance. These groups represent important targets for LDKT access interventions. Individuals with income below USD 50,000 per year have recently been shown to be 41% less likely to receive a living donor kidney transplant [44]. Low LDKT access has been extensively documented among people with less health literacy and social support, those of minoritized race and ethnicity, residents of disadvantaged neighborhoods [45], and persons of low socioeconomic status [46] and without private insurance [46]. Website interventions for LDKT access have excluded individuals with lower health or computer literacy [47]. We offered text (and email) modalities for study participation based on our formative research that text messaging was the preferred communication technology among underrepresented subgroups, such as among African American patients [48]. In the trial, only 6.4% (55/865) of KT-seekers were excluded because of having no access to email or text messaging services. Trials involving kidney patients should consider text communications to enhance diversity of enrollment.

Older persons were significantly less likely to enroll, yet more likely to complete surveys once enrolled. Non-participation in remote studies may reflect reservations about technology use or limited device or internet access [22]. Other digital studies have found that older people had lower positive attitudes towards digital health technologies [49,50]. While digital health technology is increasing, its use in older age groups remains low [50], potentially worsening health inequalities. It is therefore important to explore how digital technologies can be created which reduce this potential. Separate trial data on KidneyTIME intervention uptake showed that older age was significantly associated with a greater number of days of intervention use [51]. This finding combined with higher survey completion by older persons in the current analysis suggest that age itself does not determine the likelihood of digital study participation once enrolled. If the usability of the digital health technology is obvious, the adoption of older people with advanced kidney disease to use the technology should improve.

Early enrollment (after receiving only one electronic invitation) was similar across participant sociodemographic and health characteristics and significantly higher amongst patients invited through text message (versus email); however, the higher response was not maintained afterwards. These outcomes suggest that after electronic invitation, text users were more likely to notice or be alerted to the study invitation and initiate it sooner than email users.

Survey completion rates were 61% at 1 month, 66% at 6 months, and 62% at 12 months. Nonresponse was not due to survey break-off, which accounted for only eight incomplete surveys. Low break-off suggests the KidneyTIME surveys were user-friendly in contrast to other research which has found that typical survey websites create a variety of usability and visual problems for those who attempt to respond via mobile devices [52]. Response rates of individuals in other studies are generally near or above 68% in recent times [53]. Our slightly lower response propensities may be because we used text or email in isolation, rather than deploying some known response maximization strategies, such as a mix of contact modes (texts, emails, letters) to follow up nonrespondents [54,55]. Regarding subgroups, response rates at 1 month were similar across sociodemographic and clinical groups; whereas, response rates at later time points were significantly lower among participants who were Black or African American race and male sex, suggesting barriers once enrolled in remote trials may be experienced disproportionately across sociodemographic groups, findings similar to other research [56]. Electronic modality did not change the impact of race or sex on 12-month survey response rate, suggesting that differences in who texts did not result in differences in who responded to the survey.

The reason for non-completion of surveys was sometimes offered by participants and may have been due to not noticing the request. We attempted to mitigate these factors by providing simple processes and participant support primarily via telephone. Of those who responded to researchers’ phone calls, most requested a push notification to put the request at the top of their messages, suggesting the value of email or text messages for easing the response task and encouraging immediate response. We offered compensation for survey completion; however, there was no association of income level and survey completion status. Lower socioeconomic status has been associated with higher attrition in prior cohort studies; however, incentives have not been shown to ensure retention by all demographic subgroups [57,58].

Our technology system played an important role in enabling participants to self-report data and study staff to monitor survey delivery status, survey click and completion status, automated reminder status, and responses to non-completed results. A dedicated study staff member reviewed daily unsatisfactory completion results (non-sent surveys, unclicked links, partial surveys) and contacted participants to offer to re-send the link along with targeted advice based on the reason for non-completion. This would have been burdensome using an in-person approach, which would have required scheduling four discrete study visits or telephone calls for each participant. However, with the onus on participants to find the electronic messages, open the links, and complete the web-based surveys, remote support was critical to optimize the participant finding the electronic messages and to prevent forgetting to ensure survey completion and 66% of participants required at least one verbal reminder to complete surveys. Verbal communication, mostly by telephone, was a key facilitator to trial participant enrollment and engagement. The methods of communication were tailored to the individuals we were trying to reach. This included asking people’s preferences of how they wanted to enter the study (email, text), when they wanted to be invited (next day or after another conversation in the clinic) and using simple English language.

Limitations

The limitations are several. Translation of our methods to other studies must be considered within the context of the intervention. Enrollment was likely enhanced by the attraction of receiving information about kidney transplantation and invitations from the hospital system. Connection to a hospital system has previously been found to be critical in allowing enrollment of a demographically representative cohort in remote studies [59]. Given the single-center nature of the study, transferability of the findings to other hospital systems, such as European healthcare systems, is not known. While KidneyTIME succeeded in enrolling a demographically representative cohort, except for older age, based on transplant referral data, there are shortcomings in using referral data as a benchmark for successful representation where a disease disproportionally impacts specific populations, in this case older people. While a number of our findings including importance of verbal reminders and push notifications to ensure survey completion may be broadly applicable to remote trial models regardless of disease and domain, our findings should be interpreted with caution given the short-term length of the study. Methods to optimize long-term participant engagement in remote trials will need continued attention. Finally, though our approach achieved engagement of those traditionally neglected from research, including those unable and willing to travel to in person study visits, most participants were affiliated with the hospital network, and may not equally represent those who do not have access to transplant referral.

5. Conclusions

To explore the use of potentially more accessible options for conducting clinical research in kidney transplantation we investigated the diversity of enrollment and response rates when using email or text messaging and web-based survey system supplemented by verbal reminders within the KidneyTIME randomized control trial. This primarily remote clinical trial offered the potential to reach more representative cohorts and reduce participant time commitments. The disparities we observed in enrollment and longer-term response rates highlight ongoing barriers not alleviated by the convenience of remote models, such as older age, male sex, and minoritized race, and underscore the need for additional work to achieve equitable and representative enrollment and retention. The most critical implementation lessons highlighted the need for a text-based option for participation, verbal follow-up with patients, and offering links in push notifications to participants who could not find the previously sent email or text. Additional research is needed to further understand these barriers and identify solutions.