5.3 Trial monitoring
The key to responsive and reliable trial execution
Once the trial is underway, robust monitoring empowers your team to maintain momentum, enhance participant experiences, and deliver high-quality data that drives confident decision-making.
By staying attuned to real-time insights, study teams can respond quickly and appropriately to emerging issues, helping protect participant safety, maintain data integrity, and support reliable trial execution.
OVERVIEW
A dynamic monitoring strategy
sDHT-enabled trials require monitoring approaches that account for continuous, remote data collection. Centralized monitoring enables earlier detection of data quality issues, adherence challenges, and clinically meaningful patterns during trial execution.
To guide your efforts, apply the proportionality principle: The scale of your monitoring solution should be proportional to the risk class of the sDHT, the risk of the medical product under development, and the complexity of the [context of use (COU)]. In some cases (e.g., high-risk devices or large, multi-site trials), automation and immediate alerting are necessary. In other cases (e.g., small, low-risk observational studies), a trial coordinator manually checking logs at a specified frequency may be the appropriate and adequate solution.
“Sponsors should implement a system to manage, throughout all stages of the clinical investigation, both risks to participants (e.g., a safety problem) and to data integrity (e.g., incomplete and/or inaccurate data).
This system to manage the quality of the investigation should help ensure data integrity while safeguarding the rights, safety, and welfare of trial participants, for example, by focusing on the design of efficient clinical trial protocols, tools for identifying and tracking potential risks, and procedures for data collection and processing.
This system should include a risk-based approach to monitoring tailored to the potential risks for the specific clinical investigation. Effective
implementation of risk-based monitoring, including the prioritization of monitoring and other oversight activities directed at processes and procedures critical for human subject protection and maintaining data integrity, should help maximize the quality of a clinical investigation.
Although FDA’s regulations require sponsors to monitor the conduct and progress of their clinical investigations, FDA regulations are not specific about how sponsors are to conduct monitoring. FDA recommends that sponsors use a risk-based approach to develop their monitoring plans and to revise their monitoring plans, if needed, as the clinical investigation proceeds. This risk-based approach should be informed by the sponsor’s overarching quality management activities undertaken in the development of the protocol and associated investigational plans and should be adjusted throughout the conduct of the investigation as needed.”
– Section II (Background), p. 2
Risk-based monitoring
Risk-based monitoring (RBM) for sDHT-enabled trials applies established RBM principles to continuous, remote data streams and digitally derived measures. Effective monitoring requires attention to several interrelated principles that address technical reliability, participant behavior, and clinical or safety implications. These considerations operate in parallel and should be tailored based on trial risk, the role of the sDHT, and the context of use.
When defining monitoring considerations for sDHT-enabled trials, consider these essential principles: technical reliability and pipeline health, participant adherence and engagement, and clinical validity and safety oversight.
| Monitoring principle | What is being monitored | Illustrative monitoring signals | Typical primary owner* |
|---|---|---|---|
| Technical reliability and pipeline health | Whether data are being captured, transmitted, processed, and stored as expected | Data completeness and latency; sync failures; processing or version mismatches; unexpected data loss | Developer; data engineering or data science lead |
| Participant adherence and engagement | Whether participants are using the sDHT and completing required activities as intended | Participant adherence (e.g., minimum wear time). | Adopter / Clinical Operations |
| Clinical validity and safety oversight | Whether the data remain clinically interpretable and raise any safety or plausibility concerns | Endpoint quality, patient safety, and model stability | Investigator / Medical Monitor |
*Ownership may vary based on trial design, sponsor–developer arrangements, and the role of the sDHT in the study. Investigator” refers to the clinician responsible for participant oversight at the study site, and “medical monitor” refers to the clinician responsible for centralized review of participant safety and clinical data across the trial.
IN PRACTICE
EXAMPLES
Key components of a participant support plan
- Tiered support system: Provide participants with a clear, accessible contact for urgent issues. Ensure urgent symptom-related concerns are routed immediately to the site/investigator, while technical troubleshooting is handled by the developer/helpdesk.
- Protocols for device issues and replacement: Establish clear guidance for lost or damaged technology and rapid replacement timelines. You must ensure participants are explicitly not penalized for tech failures outside their control.
- Transparent communication: Proactively manage participant expectations regarding contact frequency, expected wear/use, and what happens if they must temporarily stop using the sDHT.
Illustrative scenarios
Phase II Parkinson’s disease trial using wrist-worn accelerometers
In a Phase II Parkinson’s disease trial using wrist-worn accelerometers to measure gait and tremor, the development team monitored data transfer performance to ensure reliable movement of data from the device to centralized storage.
Synchronization success rates were tracked continuously, with alerts triggered when failure rates exceeded predefined thresholds over a 24-hour period. These alerts enabled timely investigation and resolution of issues, such as firmware synchronization failures, helping prevent data loss and preserve the integrity and timeliness of the collected data.
Diabetes management trial using continuous glucose monitors (CGMs)
In a diabetes management trial using continuous glucose monitors (CGMs) integrated with a mobile application, the clinical operations team monitored participant adherence using centralized dashboards that tracked daily data yield and wear patterns. Participants with sustained gaps in data—such as less than 80% data availability over a 48-hour period—were flagged for follow-up.
These signals prompted timely, non-technical interventions, including automated reminders and virtual check-ins led by study staff. Follow-up conversations helped identify common barriers, such as sensor discomfort or uncertainty about proper use, enabling targeted support that improved participant engagement and overall data completeness.
Respiratory function trial using smart inhalers with embedded sensors
In a respiratory function trial using smart inhalers with embedded sensors, investigators and medical monitors conducted periodic reviews of derived respiratory metrics, such as peak flow trends, to assess clinical plausibility and consistency with the expected disease context. Extreme values or unexpected shifts prompted further review to determine whether findings reflected true clinical change, participant-level issues, or potential data processing concerns.
In parallel, monitoring workflows flagged patterns with potential safety relevance, such as sustained or unexpected declines in oxygen saturation. These signals were triaged by the investigator, who initiated appropriate follow-up in accordance with the study’s safety monitoring plan to assess participant status and determine whether additional action was needed.
Key TAKEAWAYs
Effective monitoring in sDHT-enabled trials requires a risk-based, proactive approach that addresses technical reliability, participant adherence and engagement, and clinical validity and safety in parallel. Together, these monitoring considerations support early identification of issues, appropriate escalation, and protection of data integrity and participant safety throughout trial execution.
RBM allows study teams to prioritize oversight where it matters most by supporting participant use of sDHTs, maintaining reliable data flows, and ensuring clinically interpretable endpoints.
While monitoring identifies emerging risks and anomalies, it does not by itself resolve them. The following section (5.4: Risk mitigation) focuses on how study teams should respond when monitoring signals indicate that further action may be needed.
5.3 Trial monitoring
Library resources to guide you
The sDHT roadmap library gathers 200+ external resources to support the adoption of sensor-based digital health technologies. To help you apply the concepts in this section, we’ve curated specific spotlights that provide direct access to critical guidance and real-world examples, helping you move from strategy to implementation.
Features essential guidance, publications, and communications from regulatory bodies relevant to this section. Use these resources to inform your regulatory strategy and ensure compliance.
Gathers real-world examples, case studies, best practices, and lessons learned from peers and leaders in the field relevant to this section. Use these insights to accelerate your work and avoid common pitfalls.