As the world becomes more digital, clinical research is evolving. One major change is the rise of decentralized clinical trials, or DCTs. Unlike traditional trials that require participants to visit hospitals or research centers, DCTs allow people to take part from their own homes. Potential benefits of this would include increasing patient access to clinical trials and reducing participant burden by lessening the need for travel and the physical strain of appearing in person at trial visits. This new model is helping researchers to reach more people and collect data more efficiently. But this paradigm is not without significant challenges and risks, both of which are being addressed with new data collection tools described in two recent articles by MUSC researchers in the Journal of Clinical and Translational Science.
What are decentralized clinical trials?
In a decentralized trial, the research comes to the participant, said Jennifer Dahne, Ph.D., a professor of Psychiatry and Behavioral Sciences. "In a decentralized design, we bring the clinical trial to the patient where they are, and that helps to decrease barriers to participation in clinical research," she explained. Dahne helped to develop a new system called MyTrials, which allows participants to collect and submit health data from home. She works with Gaylen Fronk, Ph.D., a postdoctoral fellow in her lab and translational scientist based out of MUSC. Together, they are building tools to make DCTs more reliable and secure.
Benefits of decentralized trials
DCTs offer several advantages. They can speed up the recruitment process and allow researchers to reach more people. Participants do not need to travel or spend time at clinics, which makes it easier for them to take part. This model also helps researchers to reach a broader group of participants, particularly from rural and other areas where access to health resources is limited, leading to better data and more effective treatments.
Challenges: Fraud and bias
Despite these benefits, DCTs also face challenges. Because everything happens remotely, it is harder to confirm the identity of participants, making DCTs susceptible to fraud. In one study, Dahne's team received 5,000 survey submissions, but 1,500 of them - about 31% - were found to be fraudulent or otherwise duplicative.
"In a decentralized design, we bring the clinical trial to the patient where they are, and that helps to decrease barriers to participation in clinical research."-- Dr. Jennifer Dahne
So, why does data integrity matter? Dahne and Fronk explained that skews in data of this nature can lead to bias in study results and inaccurate findings at the end of clinical trials, affecting larger medical and funding decisions that can ultimately sway public health. Additionally, although DCTs have the potential to reach a more representative group of participants, this is not guaranteed. Without explicit monitoring during enrollment, DCT samples may face the same bias present in traditional clinical trials. Sampling bias limits the impact of clinical research and may exclude many populations from benefiting from scientific advances. This led Fronk to ask an important question: "Can we trust the data we're collecting when we don't meet participants in person?"
CheatBlocker: A tool to detect fraud
To address this issue, Dahne developed a tool called CheatBlocker with support from a pilot grant from the South Carolina Clinical & Translational Research Institute (SCTR). CheatBlocker helps to detect fraud during the early stages of a trial by checking for duplicate screening submissions. This process occurs automatically in the background, which is particularly important for DCTs in which the pace of recruitment is accelerated. CheatBlocker works with REDCap, a secure, web-based software application widely used by researchers to collect and store study data.
QuotaConfig: Ensuring sample representativeness
In order to combat sample bias, Fronk's paper outlines the use of another tool: QuotaConfig. This monitors key characteristics during screening to ensure a representative sample is enrolled in the DCT. These characteristics could include demographic details, such as age, sex or race, as well as any other study criteria designated by the research team, for example, severity of the disease in question. Researchers can even set enrollment minimums and maximums for various characteristics. For instance, the tool can be set to make sure that at least 50% of the sample is female. QuotaConfig helps to monitor these elements in real time so that the team running the trial is certain that the sample doesn't skew too much in any one direction but, rather, accurately captures the population being studied.
MyTrials: Making data collection easier
Also, during the prescreening process, as well as once a trial begins, participants may need to submit health data called biomarkers, such as blood pressure, temperature or oxygen levels. In traditional trials, this requires a visit to a clinic. But with MyTrials, a tool developed with the support of a grant from the National Center for Advancing Translational Sciences, participants can use a smartphone app to record and send these data from home. Dahne explained that before MyTrials, participants had no streamlined process to follow. Participating remotely often meant having to download multiple data collection apps or tools and manually send information to the study team, leading to serious difficulty and complications in capturing accurate and timely data. Now, MyTrials collects everything in one place and sends it directly to REDCap, making the process easier for both participants and researchers. Another big upside is the video capture function included in the tool, which helps to confirm participant identity, ensuring data integrity.
Looking ahead
While DCT optimization remains an evolving process, Dahne and Fronk hope their tools will soon be used by scientists around the world. With funding from the National Institute on Drug Abuse, Dahne is looking to enhance biomarker collection capabilities supported by MyTrials, for example, by collecting saliva and breath carbon monoxide samples remotely. Then, her plan is to distribute the tool on a greater scale. "The big vision here is to make this platform available to investigators all over the country, all over the world, to help make their scientific methods both more feasible and more rigorous," said Dahne. "I'm hoping to get this product into the hands of other scientists whose work could benefit from it."
References
Dahne J, Wahlquist AE, Kustanowitz J, Hayden J, Natale N, Clark J. Evaluation of a Remote Biomarker Capture System Integrated with REDCap: A Decentralized Randomized Trial. Journal of Clinical and Translational Science. Published online 2025:1-20. doi:10.1017/cts.2025.10160
Fronk GE, Hawk LW, Cates AM, Clark JT, Natale N, Dahne J. Advancing Translational Science Through Trial Integrity: REDCap-Based Approaches to Mitigating Fraud and Bias. Journal of Clinical and Translational Science. Published online 2025:1-19. doi:10.1017/cts.2025.10176
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