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Why using COAs and DHTs together is the future of clinical research

In the complex, highly-regulated world of clinical research, two powerful tools stand out to collect the patient experience: Clinical Outcome Assessments (COAs) and Digital Health Technologies (DHTs). Though both COAs and DHTs are tools used to evaluate a treatment’s impact, they often exist in separate spheres, rarely integrated. We argue that combining these two approaches in the same study can help researchers gather better data and improve the likelihood of regulatory approval.

COAs and DHTs: Advantages and limitations

COAs are typically questionnaires completed by a patient or their clinician, allowing researchers to capture how a treatment impacts a patient's quality-of-life and health. However, COAs are subjective. They rely on the patients’ ability to recall their symptoms accurately. How a patient experiences their condition, and how they reflect these experiences in the questionnaire may vary depending on how they feel when completing it. 

DHTs collect health data through wearable devices, digital sensors, and mobile apps which provide objective health data that can offer unique insights. The data is robust and unbiased. However, DHTs cannot capture the impact of the changes in these datapoints on the patients’ quality of life. 

The case for using both COAs and DHTs in the same study

Despite their individual strengths, the synergy between these approaches remains largely unexplored. There are many benefits to gain from using both tools within the same clinical trial. They provide complementary insights, filling any gaps in data that may result from using either one alone.

When used together in the same study COAs and DHTs have the potential to be better than the sum of their parts. To illustrate the possibilities of combining this powerful duo imagine researchers designing a clinical trial for a major new type 2 diabetes drug. The team wants to validate the drug and give regulators sufficient data for approval. They also want to use a diverse set of the diabetic patient population.

Some clinical trial teams in this position would recruit patients and give them a weekly questionnaire. This would ask them about their diabetes symptoms and the impact of diabetes on their daily activities. The trial team gets great patient quality of life data, but ultimately that data is limited. Patients’ recall of the previous week may not be accurate, or they may bias their answers based on how they feel when answering the questionnaire. The nature of this subjective data also makes it difficult to gauge how patients’ blood glucose levels impact these symptoms. They measure how the patients subjectively feel, but how are those subjective feelings influenced in real time by their glucose levels? These limitations leave the clinical team with an incomplete picture of the drug's effects and may decrease the likelihood of regulatory approval.

Other clinical trial teams might choose to give patients a continuous glucose monitor device that tracks their glucose levels throughout the day. This gives the team precise, real-time data on how patients’ glucose levels change. But it doesn't capture the broader impact of how those changes impact the patients' quality of life. Glucose readings may improve, but what is the impact on the patients’ quality of life? 

In this hypothetical case, the team decides to measure both the medication's impact on patients’ glucose levels and its impact on patients' feelings of fatigue throughout the day. They select a patient-reported outcome that asks patients about their energy levels and physical limitations. In tandem, the study team selects a continuous glucose monitor device that tracks patients’ glucose levels throughout the day. As the trial progresses, the team has objective data on patients’ glucose levels as well as the weekly questionnaire data that provides data on how patients feel. The team could choose to deliver the COAs through a mobile app, allowing patients to participate remotely. This opens up the study to a more diverse set of participants. 

As data is analysed, the continuous glucose monitor DHT shows that patients in the trial have significant improvements in their glucose levels. In addition, the COA questionnaire data reveals that patients also experience higher energy levels and fewer side effects than other treatment options. This comprehensive data helps the clinical trial move confidently towards regulatory approval and real-world use. 

The future of patient outcomes measurement

Although this study is hypothetical today, it may not be so for long. In the future this practice may become the norm. Advances in digital health technologies and more robust tools like those offered in ICON's Outcome Measures service are enabling more clinical trials to use COAs and DHTs side by side. This integration of COAs and DHTs in clinical trials represents a significant breakthrough in our ability to capture a holistic view of treatments’ impacts. By combining patient-reported outcomes with real-time, objective health data, researchers can paint a more complete picture of a drug's efficacy. 

Using both COA and DHT data addresses the limitations of using either method alone. COAs provide crucial insights into patients' quality of life but may be subject to recall bias, while DHTs offer precise, continuous data but lack context regarding patient experiences. The synergy between these tools only enhances the quality and depth of data collected. It also enables more diverse patient population participation through remote access tools. Ultimately, this comprehensive approach strengthens the case for regulatory approval and paves the way for more effective, patient-centric treatments.

Conclusion

As we look to the future of clinical research, the combined use of COAs and DHTs stands out as a game-changing strategy. This approach yields richer, more nuanced data and aligns closely with the growing emphasis on patient-centered outcomes in healthcare. By bridging the gap between objective measurements and subjective experiences, researchers can develop a deeper understanding of treatment effects, leading to more targeted and effective therapies. As technology continues to advance and services like ICON's Outcome Measures evolve, we anticipate a new era of drug development characterised by faster approvals and more personalised treatments. Ultimately this will lead to better outcomes for patients worldwide.

By: Don Tomes, Product Manager (formerly HumanFirst)

Partner with ICON

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