Running multi-sensor wearable data in-house means owning measure design, device logistics, site training, wear monitoring, data ingestion, cleaning and analysis, each with its own people and systems. Sponsors without that capacity can work with a wearable sensor contract research organization (CRO) alongside their existing CRO, so the digital measurement workstream is covered without building internal infrastructure.
Many sponsors start a wearable program assuming the existing team will absorb it. The clinical operations group handles devices, the data management group handles files, and the biostatistics group handles analysis. This article sets out what that work actually involves, where in-house programs tend to break down, and how to decide between building and partnering.
What Does Wearable Data Analytics Involve in a Clinical Trial?
Wearable data analytics in a trial is not a single task. It is a chain that runs from the scientific question to an analysis-ready dataset. The scientific side follows five published stages:
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- Identify a Meaningful Clinical Concept
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- Select Appropriate Technologies
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- Develop Digital Biomarkers
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- Establish Validation Evidence
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- Build Digital Endpoints
The operational side runs underneath every stage. Devices have to reach sites, sites have to be trained, participants have to wear the sensors as the protocol requires, and data from several sensors has to be ingested, cleaned and processed into consistent measures. A weakness anywhere in the chain shows up at the end, usually as missing or unusable data.
What Capabilities Does an In-House Team Need?
Scientific Design
Someone has to define the concept of interest, decide which measures to capture and set the rules that turn raw signals into a digital biomarker. That requires scientists who know both the therapeutic area and sensor data. Our guide to digital biomarkers and digital endpoints explains why the distinction matters at this stage.
Device Logistics and Site Training
Devices need sourcing, configuration, shipping, replacement and return across every site and region. Site staff need training before enrollment opens, and again when staff change. The U.S. Food and Drug Administration (FDA) guidance on digital health technologies (DHTs) for remote data acquisition, finalized in December 2023, recommends that “trial participants and trial personnel should be trained on the appropriate use of DHTs.”
Wear Monitoring During the Study
Wear compliance has to be visible while the study is running, not only at database lock. That means a system that tracks each device to site and onto the participant, flags low wear early and routes the issue to someone who can act on it.
Multi-Sensor Data Ingestion
Different sensors produce different file formats, sampling rates and timestamps. A trial that combines a wrist-worn accelerometer with a second sensor needs a pipeline that aligns those streams and records exactly what was received and when.
Cleaning, Algorithms and Analysis
Raw sensor output is not a measure. Teams need wear-time rules, gap classification, algorithm version control and documented processing so that every value can be traced back to its source.
Documentation for Regulatory Discussions
FDA guidance recommends describing data management, “including collection, storage, transmission, and archiving.” The documentation burden grows with every sensor and every vendor added to the study.
Where Do In-House Wearable Programs Usually Break Down?
The failure modes are consistent across programs.
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- Compliance is found too late. Low wear surfaces at database lock, when nothing can be recovered.
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- The device comes before the question. A sensor is chosen because it is available, and the measure is retrofitted to it.
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- Data gaps are treated as random. In many populations, participants may wear sensors less on days when they feel worse. Unclassified gaps can erase the signal a study was designed to detect.
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- Algorithms change mid-study. A firmware or software update alters how a measure is computed, and nobody records which version produced which data.
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- Responsibility is split. Clinical operations owns the devices, data management owns the files, and no one owns the measure from end to end.
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- Site training fades. Turnover at sites means the people handing out devices in month nine were never trained.
Each of these is a process problem, not a technology problem. They are also hard to fix after enrollment starts. For a closer look at the compliance metrics involved, see Wear Time, Valid Days and Analyzable Data.
How Do Building and Partnering Compare?
| Build in-house | Partner with a wearable sensor CRO |
|---|---|
| Hire or train scientists for measure design | Scientific team already in place for the digital measurement workstream |
| Set up device shipping, replacement and returns | Device logistics and site training handled by the partner |
| Build or buy a wear-monitoring system | Wear compliance monitored in real time during the study |
| Develop pipelines for each new sensor | Data from multiple sensors ingested and cleaned in one process |
| Full internal control and capability over time | Works alongside the sponsor’s CRO, with the sponsor setting the scientific direction |
When Does Building In-House Make Sense?
Building can be the right call. A sponsor running wearables across a large portfolio, with a dedicated digital health group and long-term investment behind it, may want the capability inside the company. The same is true where a single measure will be reused across many studies in one indication.
Partnering tends to fit when a sponsor is running its early studies with sensor-based measures, when a trial combines several sensors, or when the internal team would need to be assembled for one program. The question is less about cost than about whether the organization can own every link in the chain for the life of the study.
What Has a Published Case Study Reported?
In a global cystic fibrosis study described in an April 2025 case study on the VivoSense site, sensor-based DHTs monitored physical activity, sleep and cough across 200 devices at 18 sites worldwide. The case study reports “99% data availability” and “94% wear compliance.”
Those figures describe that study only. They are not a result every study will reach.
How Does VivoSense Work Alongside a Sponsor’s Team?
VivoSense is a wearable sensor CRO. It does not make devices and it does not replace the sponsor’s CRO. It covers the digital measurement workstream end to end, alongside the sponsor’s existing partners.
In practice, starting from the sponsor’s clinical question, VivoSense helps the sponsor choose the right device based on the disease state and the population of the patients, and decide which measures to capture. VivoSense ships the devices and trains the sites. A purpose-built cloud platform monitors each device getting to site and onto the participant, and tracks wear compliance in real time against the protocol. Data from multiple sensors and devices is ingested, cleaned and run through VivoSense algorithms to produce formatted, regulatory-ready data packages for the study team.
VivoSense’s services are grouped under three areas:
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- Design & Configure
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- Generate, Collect & Clean
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- Interpret & Deliver Insights
See Our Solutions for more, or read What Is a Wearable Sensor CRO?
Frequently Asked Questions
What platforms help process multi-sensor wearable data without internal information technology (IT) infrastructure?
Sponsors without internal infrastructure often work with a wearable sensor CRO. The CRO selects and deploys the sensors, monitors wear during the study, and ingests and cleans data from multiple sensors into analysis-ready outputs, working alongside the sponsor’s existing CRO.
What is wearable data analytics in clinical trials?
It is the process of turning raw sensor data into trial-ready measures. That includes wear-time rules, classification of data gaps, algorithm processing and documentation that traces every value back to its source.
What is the biggest risk of running wearable data in-house?
Low wear compliance discovered too late. If no one monitors wear during the study, gaps surface at database lock, when the data cannot be recovered.
Does a wearable sensor CRO replace a sponsor’s CRO?
No. A wearable sensor CRO works alongside the sponsor’s CRO and covers the digital measurement workstream only.
What should a sponsor ask before deciding to build or partner?
Ask who will own the measure from concept to analysis, how wear compliance will be seen during the study, how data from different sensors will be combined, and how algorithm versions will be controlled.
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