Karl McEvoy, PhD
Vice President, eCOA and Patient Technologies,
YPrime
Aubrey Verna
Senior Product Director,
YPrime
As GLP-1 therapies expand across indications like obesity, cardiovascular disease, and rare metabolic conditions, clinical trial teams are faced with critical decisions about how to monitor glucose effectively. Continuous glucose monitoring (CGM) and blood glucose meters (BGM) each offer distinct advantages—but the choice of device is just the beginning.
In trials that include glucose monitoring, collecting the data is only step one. Ensuring that data is reliable, compliant, and operationally sustainable requires alignment across clinical, technical, and operational stakeholders—from site training and patient adherence to system integration and data visualization. In a recent article in Applied Clinical Trials, we explored the core differences between CGM (continuous glucose monitoring) and BGM (blood glucose meters), highlighting their respective roles in clinical trials—especially those evaluating GLP-1 therapies. Now, we turn our attention to what happens after device selection: managing the data, ensuring patient compliance, and supporting global operations.
In clinical trials that include glucose monitoring, collecting data is only the beginning. Making that data usable—and reliable—requires careful planning across clinical, operational, and technical teams.
Data Volume: What Happens When You Scale?
Let’s start with the numbers. A CGM device collecting data every five minutes will generate approximately 105,120 data points per patient during a year-long study—compared to fewer than 1,825 data points per patient per year with BGM.
This creates two immediate challenges:
CGM data enables rich insights into glycemic variability, time in range, and safety indicators, but only if the infrastructure supports real-time ingestion, transformation, and review. Sponsors need to ensure that both data volume and data use are accounted for in protocol planning and vendor selection.
Minimizing Data Loss
Data loss is a risk in clinical research, but the implications vary widely depending on the device used:
Patient Compliance: Finger Stick Fatigue vs Sensor Adherence
Patient burden plays a central role in device performance.
From pediatric trials—where reducing finger sticks improves quality of life—to elderly populations already experienced with BGM, clinical trial teams must consider user experience and lifestyle fit when selecting a monitoring strategy.
Global Reach and Regulatory Differences
Device availability, cost, and acceptance vary by region. While BGM is widely accessible with standardized models, CGM adoption requires more nuanced planning. Although several CGM devices have received FDA and other regulatory approvals, their use in label-enabling trials has typically been supported by concurrent BGM data. Reimbursement pathways, site familiarity, and patient comfort with CGM can also differ significantly across countries.
BGM also offers advantages in global logistics and standardization, whereas CGM requires a more nuanced deployment plan. Sponsors must evaluate device sourcing, training, and support across all trial regions—not just their primary sites.
CGM Isn’t a One-Click Upgrade
It’s tempting to view CGM as a more advanced, more complete data solution, and in time it may be the recommendation, but as an industry we aren’t there just yet.
When evaluating CGM or BGM as part of an eCOA strategy, clinical trial sponsors must ask:
In trials where point-in-time glucose is sufficient for primary endpoints, BGM remains a trusted and efficient choice. But in studies measuring glycemic control, variability, or response over time, CGM offers value and depth of data that is hard to ignore.
Looking Ahead: Predictive Models and Next-Gen Monitoring
We’re also beginning to see CGM data feeding into predictive algorithms—where AI models flag potential glycemic events before they occur. Some sponsors are already combining CGM and patient-reported outcomes to build smarter, more responsive trials.
Still, we’re not at the point where CGM fully replaces BGM. In fact, most CGM manufacturers recommend BGM for calibration, especially in the first 24 hours or when symptoms might not match sensor data.
Bottom line? Clinical trial teams shouldn’t be asking “BGM or CGM?” just yet. They should be asking: What does my protocol need, and how do I support it—clinically, technically, from a data infrastructure standpoint, and from a regulatory point of view.
No matter which device is selected for your clinical trial, success depends on more than just sensor specs. It’s about aligning endpoints with infrastructure, simplifying the participant experience, and ensuring every data point can be used to drive better outcomes.
Whether you’re designing your first glucose-enabled trial or scaling up for multiple GLP-1 indications, the right strategy starts with asking the right questions.
YPrime’s experts are here to support you—from eCOA integration with your devices, and beyond with data visualization.
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