Wearables have moved well beyond pilot projects and proof-of-concept studies. Many sponsors are already exploring how continuous activity, physiological, and behavioral data can strengthen their clinical programs. The question now is not whether wearables can collect data. It is where that data truly adds value and how to incorporate it without adding operational complexity.
Not every protocol calls for a smartwatch or activity tracker. But in the right scenarios, wearable data can deepen insight into patient function, provide context between site visits, and support more flexible trial designs. When that data flows directly into a unified clinical platform like TrialKit, sponsors can manage wearable streams alongside electronic data capture and electronic clinical outcome assessments with full visibility and control.
Below are the trial designs and therapeutic settings where wearables tend to make the most sense, along with practical considerations for getting started quickly and confidently.
1. Conditions with Fluctuating or Episodic Symptoms
Some diseases do not behave consistently from one clinic visit to the next. Symptoms can shift hour by hour or day by day.
Neurological disorders such as Parkinson’s disease or multiple sclerosis, migraine, asthma, and chronic obstructive pulmonary disease (COPD) are clear examples. A single in-clinic assessment may miss important variability. Continuous or high-frequency wearable data can reveal patterns that episodic visits simply cannot capture.
Activity levels, tremor patterns, heart rate variability, and sleep disruption can provide a more complete view of disease burden. Over time, these data streams may help identify subtle changes in progression or response to treatment.
In these settings, wearables help close the gap between how patients feel in daily life and what is observed during scheduled visits.
2. Functional and Mobility-Based Endpoints
In many therapeutic areas, the core question is not only whether a biomarker lab figure changes, but whether a patient functions better.
Musculoskeletal studies, orthopedic recovery trials, chronic pain, and frailty research all benefit from objective functional measures. Step counts, gait characteristics, and overall activity levels provide quantifiable insight into mobility and daily function.
These measures can complement patient-reported outcomes collected through electronic clinical outcome assessments (eCOA). They can also provide an objective layer that strengthens the interpretation of subjective reports.
When sponsors want to demonstrate real-world functional improvement, wearable data can play a central role.
3. Sleep, Fatigue, and Behavioral Outcomes
Sleep and fatigue are notoriously difficult to measure accurately through recall alone.
In psychiatric studies, neurology trials, oncology supportive care, and post-viral conditions, sleep quality and activity-rest cycles often reflect meaningful treatment impact. Wearables can capture duration, fragmentation, and circadian patterns in a way that questionnaires alone cannot.
Behavioral signals such as daily movement patterns may also correlate with mood, energy, or overall well-being.
When sleep and fatigue are central to the indication, wearable integration becomes a logical design choice rather than a technology experiment.
4. Decentralized and Hybrid Trial Designs
Wearables are especially valuable in decentralized or hybrid trials.
Rare disease studies, pediatric populations, and geographically dispersed participants often benefit from reduced site visits. Continuous remote data capture allows sponsors to maintain visibility between visits without increasing patient burden.
When wearable data is combined with electronic data capture, or EDC, and remote eCOA, sponsors can create a more patient-friendly design while maintaining data integrity.
In these scenarios, wearables solve a practical operational challenge as well as a measurement one.
Getting Started Quickly and Confidently
Once a sponsor identifies a clear use case, execution becomes the next challenge.
The first step is aligning wearable data with endpoint intent. Define what you want to measure and how it supports the study hypothesis. Then select devices and metrics that directly support that goal.
Operationally, integration matters as much as device choice.
TrialKit enables wearable and health activity data to flow directly into the same platform used for electronic data capture and eCOA. That means wearable streams are not managed in a separate silo. Study teams gain centralized oversight, consistent data standards, and real-time visibility into adherence and completeness.
Because wearable data becomes part of the core study dataset, sponsors can define derived variables and calculated fields within the system. Data managers and clinical teams can monitor quality in real time. Audit trails and traceability are preserved within a validated clinical environment.
This unified approach reduces reconciliation burden and shortens startup timelines. Sponsors do not need to build a parallel data infrastructure to pilot wearable endpoints. They can extend their existing platform to support new measurement strategies.
The Bottom Line
Wearables make the most sense when they address a real measurement gap.
Fluctuating conditions, functional endpoints, sleep and behavioral outcomes, and decentralized designs are strong candidates. In these contexts, continuous real-world data can materially strengthen a trial.
With a purpose-built clinical platform like TrialKit, sponsors can move from concept to implementation without unnecessary operational friction. Wearable data becomes integrated, traceable, and analysis-ready.
For more information, contact us today.
