November 11, 2020
Covid activity assessment
Clinical Trials & Research
August 29, 2022
The Test Us At Home study provided important insights into the efficacy of serial at-home testing for new-onset COVID-19 infection. The decentralized nature of the study, enabled by the MyDataHelps™ digital clinical trial and research platform, allowed the team to reach a broad demographic, screen participants, create automations in surveys, and facilitate kitting in a streamlined workflow. Ultimately, the platform allowed for rapid operationalizing of a national decentralized study design with minimal staff burden and an innovative, adaptable protocol.
The Food and Drug Administration (FDA) recently released updated guidance regarding at-home COVID testing practices. This guidance is a result of a series of research projects, including Test Us At Home (TUAH), powered by CareEvolution’s MyDataHelps™ digital clinical trial and research platform.
In the TUAH study, researchers at the University of Massachusetts Chan Medical School (UMass Chan) and the National Institutes of Health (NIH) Rapid Acceleration of Diagnostics (RADx) program partnered with CareEvolution to evaluate the performance of serial use of three different rapid antigen tests to detect a new-onset SARS-CoV-2 infection. The study involved performing a rapid antigen over-the-counter (OTC) test and molecular comparator PCR test on the same day roughly every 48 hours during a 15-day testing period. Learn more about the study protocol and mechanics in this paper.
CareEvolution’s learnings and findings from supporting this national decentralized study, with a rapid response time facilitated by our flexible suite of features, are shared below.
Conceptualizing an innovative recruitment model
Using an innovative recruitment model to enroll participants and harness macro COVID trends, the team was able to recruit over 7,000 participants in 4 months from 44 states. The recruitment model in this study utilized a two-tiered eligibility assessment system: core eligibility, and preferred criteria eligibility.
The core eligibility was composed of fixed criteria that a participant was required to meet in order to be a potential candidate for the study. These factors included age, English or Spanish proficiency, and physical limitations that would impact a person’s ability to complete the study protocol.
The preferred criteria eligibility was a secondary level of assessment that could further narrow down who might be eligible for the study at a given time. These criteria included geography, vaccination status, ethnicity, and age. Most notably, the study protocol allowed for adjustments in the preferred criteria without additional IRB approval. Participants who met the core eligibility, but not the preferred criteria at the time of their enrollment were given the opportunity to join a waitlist for the study.
Using dynamic eligibility criteria to curate the participant pool
The TUAH model of a two-tiered eligibility assessment system with adaptable second-tier criteria enabled the team to stay nimble and take advantage of changing recruitment needs on the fly. The study launched with no additional restrictions on the core eligibility. As the needs of the study changed, preferred criteria were employed to focus enrollment on specific populations. Throughout the 4 months of enrollment, the prioritization of populations spanned across:
Utilizing the dynamic eligibility mechanism improved the representation of underrepresented groups in the study population. Curating a balanced cohort was a specific focus of the collaboration with the FDA.
Spurring recruitment through multiple channels
The digital and decentralized nature of the TUAH study, combined with existing MyDataHelps™ recruitment and outreach tools, allowed for discovery of and interaction with the study through multiple channels, ultimately enhancing the ability to recruit new participants throughout the study’s duration. Digital recruitment methods utilized social media and news coverage to generate interest in the study. In addition, partnership with community organizations was an important tool to create awareness and facilitate enrollment in the study. MyDataHelps™ was configured to identify participants belonging to these community groups and give them streamlined access to the study, bypassing the preferred criteria evaluation. This enabled excitement surrounding the study to spread throughout a community and boost enrollment through organic means.
One of the key strategies for boosting recruitment at the onset of the study was launching a partnership with the Marching Band at University of Wisconsin at Eau Claire prior to a stretch of traveling required for their football season. This spurred rapid enrollment of dozens of participants and facilitated early adjustment of key logistics, as needed.
In addition to partnerships, the waitlist became a key source of recruitment throughout the study. People were invited to join TUAH from the waitlist to meet participant diversity and COVID positivity requirements, as needed. By controlling the rate of enrollment early on, the waitlist model ensured that awareness of the study continued to spread across communities before recruitment targets were met. This assisted tremendously in improving the diversity of the candidate pool and optimizing the COVID positivity rate amongst participants.
Overall, the waitlist enabled researchers to continue to enroll at a healthy pace while simultaneously enforcing stricter requirements for study eligibility.
Complex research protocols run the risk of overburdening coordinator resources as their responsibilities add up. Managing onboarding, enforcing study adherence, and supporting participants throughout the study are common drains on research staff time and effort. This becomes acutely problematic in a study like TUAH, when enrollment occurs at such a high volume and velocity—supporting the enrollment of 3700 participants in one month is an incredibly large burden for staff.
Protocol automations, powered by the MyDataHelps™ robust rules engine, enabled the study team to achieve challenging enrollment targets. In fact, according to the study coordination logs, total direct participant-coordinator phone calls averaged just over 1 hour per day and consisted primarily of returning molecular test results to participants, providing guidance after a positive test, and testing reminders.
By reducing the burden on coordinators using automations to achieve scale, MyDataHelps™ allowed study stakeholders to conduct the study at 1/6th of the cost it would have been had the test manufacturers elected to conduct their own site-based studies. The platform was able to help 3 companies simultaneously collect sufficient data in 4 months, in contrast to the anticipated timeline of 8 months.
Automated delivery of notifications to promote testing adherence
In a more conventional, paper-based model of research, coordinators are required to track participants throughout the study protocol phases, making phone calls, sending emails, and performing follow up visits to promote completion of tasks. These practices are highly reactive and time consuming, resulting in lower overall study adherence and participant burnout. As a digital clinical trial and research platform, MyDataHelps™ has a built-in, no-code logic engine that can automate the delivery of notifications to participants via SMS, email, and push notifications to promote adherence.
The TUAH study employed testing windows to encourage participants to take their tests 44-52 hours after the previous test was completed. MyDataHelps™ was used to send notifications to participants at the 44-hour mark, followed by subsequent notifications every two hours until the participant completed their tests or reached the 52-hour mark. Coordinators were only required to monitor the list of participants who hadn’t completed their test with only 1 hour remaining in their testing window. This largely automated approach resulted in minimal resource allocation to manual calls for participant adherence.
3rd party integrations to manage shipment
A major challenge associated with decentralized studies is the distribution of study materials to participants located across the country. For the TUAH study, participants required both rapid antigen tests from the assigned manufacturer and molecular tests from Quest Diagnostics. Manually assembling participant packages on enrollment was logistically infeasible at this scale. Thus, MyDataHelps™ was utilized to facilitate integration with a national fulfillment partner such that rapid antigen tests were shipped directly to a participant’s home.
Furthermore, MyDataHelps™ enabled curated data exports that assisted the study team in building an automated workflow for ordering PCR tests to the participant through Quest. This relieved staff from spending hundreds of coordinator hours on fulfillment, allowing the team to focus on ensuring participants have a smooth study experience.
Algorithmic assignment of test kits
Three at-home tests were used in the TUAH study: Quidel QuickVue At-Home OTC COVID-19 Test, Abbott BinaxNOW COVID-19 Antigen Self-Test, and BD Veritor At-Home COVID-19 Test. The goal was to recruit for each of these tests equally to generate sufficient data for the FDA to accurately assess the performance of each test. However, when operationalizing test assignments, challenges arise when multiple participants reside in the same household or community where mixing or sharing tests is a possibility. Additionally, the BD test required the use of a mobile app to read their tests, which did not have widespread compatibility across Android and iPhone. These factors added to the complexity of test assignment.
The MyDataHelps™ platform is designed to handle complex logic queries, which allowed the team to apply the constraints of the test assignment to each participant when they enrolled in the study. In addition, the participant experience was curated to refer to the specific test to which the participant was assigned. By showing them pictures and names of their specific test, participants were pre-emptively primed with what to expect, creating a more smooth participant experience.
IRB collaboration over time facilitates advancements in digital clinical research practices
Building an innovative research study starts far before the first participant is enrolled. Partnership and collaboration with stakeholders like the Institutional Review Board (IRB) can be fostered over time to ensure conventional regulations are able to evolve with rapidly changing technology. Before the TUAH study, the model for digital research followed a more traditional process, in which eligibility was assessed through simple check box questions leading to a signature being collected for study consent. Throughout 5 previous projects with CareEvolution, the UMass Chan team invested in the relationship with their IRB by sharing approaches and working together to innovate. This led to the adoption of digital eConsent and the dynamic eligibility used in the TUAH study.
Ultimately, the success of the study relied on creating a participant pool that had the characteristics necessary (e.g., diversity, COVID positivity) to generate sufficient data that could yield useful insight, which was only possible within the study’s condensed duration through this innovative model. The two-tiered eligibility assessment system with adaptable preferred criteria enabled the team to stay nimble and quickly take advantage of changing recruitment needs.
A reliable audit infrastructure increases confidence that remote participants are adhering to the protocol
The MyDataHelps™ platform collects real-time detailed audit information on each participant’s activity throughout the study lifetime. Dr. Apurv Soni remarked that “by having the timestamps for all data elements, data generated from this study met FDA requirements for data provenance in a much more comprehensive way in comparison to traditional site-based studies.” This detailed information also helped the study team identify participants that were not completing study activities and facilitated early intervention
The platform’s ability to capture images of the tests also allowed for understanding more about real-world interpretation of at-home tests in ways that traditional clinical studies could not. The study included test result image capture as a mandatory component of the test collection process, which allowed for a comparison of user interpretations of test results and those of an expert. This verification of rapid antigen test results for all participants also increased confidence in protocol adherence.
Improve communication with participants
Clear, effective communication with participants is an important component of a positive participant experience. Thus, it is advised that future iterations of this study model take further advantage of the digital nature of the study and increase clarity about the study expectations and progress. This could be manifested by:
Ultimately, these enhancements would allow the participant to become more self-sufficient and reduce coordinator burden.
Reduce monitoring requirement for coordinators
The MyDataHelps™ designer interface generates curated lists of participants based on logic criteria, which the research team can monitor to determine when coordinator action is necessary. The automated calculation of these lists significantly reduces coordinator burden, but the 24-hour monitoring of these lists remains tedious.
Following the completion of the TUAH study, MyDataHelps™ added a feature that can now be configured to send an email to the research team when a participant meets a criteria that requires a coordinator action. This new functionality can further reduce coordinators’ monitoring requirements in future iterations of this study model.
Improve the waitlist
Many participants who wanted to join the TUAH study did not meet the preferred criteria at enrollment because they were not in close contact with a confirmed COVID-positive person within the last 7 days. While they were invited to join the waitlist, the research team had no mechanism for identifying participants on the waitlist who may have become eligible to participate given the current preferred criteria requirements. In the future, it would be beneficial to develop a mechanism for participants to indicate when their COVID status changed. This would enable researchers to contact participants at the optimal time and maximize opportunity for effective enrollment.