In our premiere QCast episode, co-hosts Tom and Jullia explore the use of wearable technologies in clinical trials, revealing how smartwatches, biosensor patches, and even implantable sensors are reshaping data collection and patient engagement. From real-time monitoring to and decentralised study designs to seamless data integration and rigorous device validation, this conversation uncovers practical strategies for sponsors and CROs to utilise wearables for more efficient, inclusive research. Whether you're driving digital health innovation or navigating regulatory compliance, learn how Quanticate's biometric expertise and data-driven dialogue can help your next trial achieve breakthrough insights.
🎧 Listen to the Episode:
Key Takeaways
- Four Core Wearable Categories: Smartwatches, fitness bands, and implantable/ingestible sensors each serve distinct trial needs – balancing usability, cost, data fidelity, and long-term monitoring.
- Real-Time, Patient-Centric Data: Continuous streams of physiological data (heart rate, glucose, sleep patterns) reduce reliance on episodic site visits, improve participant retention, and support adaptive trial models.
- Seamless Data Integration: Wearable data must flow directly into platforms like EDCs and data workbenches. Evolving standards such as ICH E6 (R3) and interoperable infrastructure are key to maintaining data integrity and accelerating decision-making.
- Rigorous Validation & Quality Assurance: Even CE-marked or FDA-cleared devices require endpoint-specific benchmarking against clinical gold standards to ensure accuracy for primary and secondary outcomes.
- Advanced Analytics & Personalisation: Machine learning and AI help parse through high-volume wearable datasets – identifying personalised baselines, flagging early safety signals, and facilitating predictive insights.
- Privacy, Security, & Compliance: Adherence to UK GDPR, the Data Protection Act, and HIPAA (US), as well as encryption and transparent consent processes, are foundation to participant trust and regulatory alignment.
- Training & Participant Compliance: User education, intuitive device design, and ongoing support ensure wearables are worn, charged, and synchronised correctly throughout the study.
- Real-World Applications: Proven use cases in cardiology (continuous ECG), diabetes (CGMs), sleep research, oncology (fatigue tracking), and chronic disease rehabilitation demonstrate wearables' impact on diagnosis, safety monitoring, and patient quality of life.
- Regulatory Landscape & Harmonisation: The MHRA (UK), NICE (NHS), and FDA (US) provide evolving guidance on digital health in trials. Global harmonisation remains a priority for steamlined multi-region studies.
- Future Outlook: Advances in sensor technology, deeper EHR integration, and expanded decentralised trial designs will further elevate wearables as indispensable tools for modern clinical research.
Full Transcript
Jullia
[00.00.01]
Welcome to QCast, the show where we explore the trends, technologies, and thinking shaping the future of clinical research. I’m Jullia.
Tom
[00.00.10]
And I’m Thomas. In this episode, we’re taking a closer look at the use of wearable devices in clinical trials. Why don’t you start things off for us Jullia as there is plenty to talk about.
Jullia
[00.00.22]
Sure. It's striking how deeply integrated technology has become in the health and life sciences space. These days, data is central to almost every aspect of clinical care and research. Data collection and analysis are now fundamental pillars of clinical trial design and execution. And there's been a clear progression toward placing the patient experience more centrally in how research is conducted. With tools that extend beyond the clinic, we can now collect more representative data from real-world settings, which adds valuable depth to the clinical picture. Wearables are a major factor in this evolution. I also just want to add, they've steadily moved from consumer novelty to validated tools in clinical research – and the scope is broad. We’re talking everything from widely used smartwatches to more advanced tools like biosensor patches or even implantable sensors, which are starting to see trial applications. These are increasingly being treated as legitimate instruments for data collection for monitoring the likes of heart rate, sleep, physical activity, and more. For participants, that can translate to fewer clinic visits and reduced disruption. Overall, it’s a meaningful step toward reducing patient burden and improving participation.
Tom
[00.01.39]
Thank you Jullia. And there’s still so much more to say. So for this discussion, we’ve compiled insights from across the field to offer a structured look at how wearables are being used in clinical trials today. Our goal is to provide a clear, practical overview of how this space is maturing and what we can anticipate for the near future.
Jullia
[00.02.06]
Alright, let’s head straight into breaking down the primary types of wearables currently in use. So, first up we’ve got smartwatches, which I’m sure many of us are familiar with. Smartwatches now offer more than just timekeeping as they also monitor heart rate, sleep data, and even have ECG capabilities. And they're typically easy to use, which supports consistent engagement across study participants. I suppose it’s that combination of technical sophistication and familiarity that makes them compelling for continuous, non-invasive monitoring. This is particularly handy when the study requires participant interaction, such as responding to prompts.
Tom
[00.02.46]
Exactly. Moving on, we’ve got fitness bands. Similar to smartwatches, these are also pretty well-known amongst a lot of people. They track step counts, sleep patterns, and basic metabolic data. Although they might not offer the full range of advanced metrics compared to some devices, these come in handy for broader monitoring across big populations as they’re often very cost-effective.
Jullia
[00.03.13]
Next we have biosensor patches, which are compact and purpose-built. These are tailored to monitor specific metrics like glucose levels or cardiac rhythms continuously. And they often stream data to the cloud in near real time. This immediacy can be useful for more responsive trial designs, particularly where continuous monitoring is needed. As such, it’s a more targeted tool than general-purpose trackers as they tend to focus on fewer, more specific indicators with high fidelity.
Tom
[00.03.42]
Finally, we’ve got implantable or ingestible sensors. While these are still a bit niche, they are gradually entering certain trial contexts. They’re still maturing, but their ability to gather internal physiological data over time is promising for clinical trials that require deep biological insights.
Jullia
[00.04.03]
So that about covers the main types of wearable devices. But what about how they’re actually used in clinical trial data management?
Tom
[00.04.11]
That’s what we’re moving onto next. The use cases for these types of wearables typically fall into three areas, with the first being real-time data collection. Instead of episodic data from site visits, you're getting a steady stream. This includes heart rate variability, sleep patterns, and glucose dynamics across daily life. It’s this shift that enables us to track trends and responses over time, rather than just isolated data points. And this visibility can reveal effects that traditional check-ins might miss.
Jullia
[00.04.43]
And I think another important use case to highlight is that wearables support patient-centric trial models. It’s often found that when patients can participate with less disruption, it improves retention and engagement. This kind of shift toward decentralised models is heavily promoted by this kind of tech. Wearables open access, especially for people who live far from trial sites or face mobility challenges. This, in turn, ultimately makes clinical trials more inclusive overall.
Tom
[00.05.12]
Yeah. Couldn’t have said it better myself. Alright, finally we’ve got data integration. Wearables produce a lot of data that needs to tie into broader clinical trial systems. Integrating wearable data into platforms like EDCs ensures researchers have a unified, real-time view of the trial dataset.
Jullia
[00.05.33]
It’s interesting you mention that because the industry is actively moving toward standards for this kind of data integration, referencing, for example, the ICH E6 R3 update. This revision reflects recognition by regulators that digital health technologies need strong, consistent validation frameworks. And with that, I suppose it brings us to the question of data quality. With any wearable used in a clinical setting, they key question remains: can we trust the data it generates?
Tom
[00.06.01]
It's a valid concern. Many of the devices used in trials are medically regulated, either CE-marked or FDA-cleared. But for clinical research, device validation has to go a step further. Even if a device is cleared for consumer or clinical use, researchers still need to validate its performance against gold-standard clinical tools for the specific endpoints they're studying. This might mean side-by-side studies comparing wearable-generated data to established clinical metrics, particularly when outcomes rely on high data precision. And when validation is done appropriately, the value of wearables becomes much more tangible. You're no longer relying on patient recall or sparse data points.
Jullia
[00.06.51]
Yeah, because now you’re capturing objective, continuous data that can strengthen the overall dataset and potentially surface patterns that wouldn't be evident from site-based assessments alone. Not to mention, it also reduces manual data entry, which lowers the risk of transcription errors and saves time for trial staff. This contributes to greater operational efficiency and supports real-time monitoring. And as trial designs become more complex, the ability to respond to early signals or emerging patterns becomes more important. Real-time, high-frequency data can support that.
Tom
[00.07.25]
However, doesn't this also introduce challenges?
Jullia
[00.07.29]
Yeah, it does. High data volume needs thoughtful planning. Systems need to handle not just the storage, but the filtering and analysis. That’s where machine learning models and advanced analytics are starting to play a more significant role. These tools can help parse through the signal and identify emerging insights without overwhelming research teams..
Tom
[00.07.51]
I think it’s also worth noting that from this we’re also seeing movement toward more personalised insights during trials, such as detecting deviations from individual baselines rather than population averages. This can offer a nuanced view of patient safety and efficacy signals, which could ultimately support more adaptive trial models. Still, the potential of this is only realised if the supporting infrastructure is in place. This includes the likes of secure data pipelines, interoperable systems, and of course, compliance with data protection regulations – which leads directly into privacy and governance, which are core pillars of any data strategy. Regulatory frameworks like GDPR in the UK and Europe, and HIPAA in the US, set clear expectations for how health data must be handled..
Jullia
[00.08.41]
Yeah – and it's worth emphasising that since wearables often collect highly sensitive, continuous data, encryption, access controls, and clear consent frameworks aren’t optional but rather foundational. And compliance goes hand in hand with maintaining participant trust. If patients don’t feel confident in how their data is handled, engagement will suffer. Following on from that, there's also the issue of participant compliance with the device itself. Even the most sophisticated wearable is only useful if worn correctly and consistently, which is exactly why training and support matter more than anything here. Participants need to understand how to use the device, how to charge it, and how to troubleshoot. User experience design can make a big difference here too.
Tom
[00.09.28]
And finally, as we've mentioned earlier, managing high volumes of data requires planning. You can’t expect researchers to sift through raw sensor data manually. That’s where structured data models, automated cleaning, and intuitive visualisation tools play a big role. These ensure data is actionable without being overwhelming.
Jullia
[00.09.53]
Exactly. Alright, now let’s talk about real-world use cases where wearables have already demonstrated their value. Cardiology is a strong example. Devices that allow for continuous ECG monitoring can capture arrhythmias that might be missed during routine in-clinic assessments as these events often happen outside scheduled visits. Continuous capture using wearables adds a dimension to clinical data that improves both diagnosis and safety monitoring.
Tom
[00.10.22]
I think it’s also worth mentioning, another established use case is diabetes, since continuous glucose monitors are well embedded in both clinical care and research. They've provided a successful model for how wearables can inform real-time decision making. And that success has helped pave the way for broader adoption of digital health tools. More use cases include the likes of sleep disorders where wearables can offer more objective data for metrics like tracking duration, interruptions, and even sleep stages. Then, in oncology, we’re seeing applications like monitoring fatigue and activity levels, helping to assess how patients tolerate treatment over time.
Jullia
[00.11.08]
Let’s not forget that more broadly, wearables are also finding roles in chronic disease management and rehabilitation such as tracking recovery after procedures or monitoring neurodegenerative conditions like Parkinson’s. And even beyond the individual level, aggregated anonymised data from large populations can inform public health and epidemiology. This opens opportunities in population health analysis and proactive policy planning.
Tom
[00.11.36]
From a regulatory standpoint, different geographies take distinct approaches. In the UK, the MHRA oversees device safety, while NICE provides evaluation on cost-effectiveness for the NHS context. In the US, on the other hand, the FDA has issued guidance on digital health technologies, emphasising strong validation and fit-for-purpose evidence. This is mirrored by the need for secure privacy frameworks, namely the GDPR and the Data Protection Act 2018 in the UK and HIPAA in the US, as we touched on slightly earlier,
Jullia
[00.12.16]
It’s also important to note that HIPAA applies based on the entity. Therefore, not every device manufacturer falls under its scope unless acting as a business associate. But even when not directly regulated, there's a strong ethical and reputational obligation to apply best practices in data stewardship.
Tom
[00.12.36]
As we look ahead, wearables are likely to continue evolving. We’re expecting to see more refined sensors, improved accuracy, and integration into routine clinical data ecosystems. And with that, we’ll likely see more decentralised trial designs, greater use of the likes of AI for predictive analytics, and tighter integration with electronic health systems.
Jullia
[00.13.06]
Therefore, for sponsors and CROs, the focus needs to remain on utilising these tools responsibly, to support better data, more inclusive participation, and ultimately, improved trial outcomes. It’s not about the novelty of the technology, but rather about the evidence, the execution, and the ability to scale it within a thorough framework. That’s all for today’s episode on wearables in clinical trials. We hope it’s given you a clearer view of how this technology is influencing the way trials are designed and delivered. Of course, there’s always more to explore, from new sensor capabilities to evolving regulatory frameworks, but one thing is clear: wearables are becoming a core part of modern clinical research.
Tom
[00.13.51]
If you found this discussion useful, don’t forget to subscribe and share. And if you’d like to learn more about how Quanticate supports clinical trials with data-driven solutions, head to our website or get in touch.
Jullia
[00.14.04]
Thanks for joining us. We'll keep these discussions coming and have plenty more episodes lined up in the coming months.
Tom
[00.14.11]
Until next time, take care.
About QCast
QCast by Quanticate is the podcast for biotech, pharma, and life science leaders looking to deepen their understanding of biometrics and modern drug development. Join co-hosts Tom and Jullia as they explore methodologies, case studies, regulatory shifts, and industry trends shaping the future of clinical research. Where biometric expertise meets data-driven dialogue, QCast delivers actionable insights and expert perspectives to power your decision-making.
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