In a common disease, a trial can often draw on a large pool of eligible participants. In a rare disease, that pool may not exist. The population is small, spread across many countries and often includes children.
Every clinic visit asks more of families who may already travel long distances for specialist care.
Digital measures collected through wearable sensors are one approach sponsors are exploring in rare disease clinical trials. They can add information from daily life, but sponsors must build the case for any specific measure with evidence from the population that will use it.
What Makes Rare Disease Clinical Trials Different?
Rare disease clinical trials work with small populations, limited knowledge of how the disease progresses and assessments that were often designed for other conditions. In the United States, the Orphan Drug Act defines a rare disease as “a disease or condition that affects less than 200,000 people in the United States.” Many rare conditions affect far fewer people than that.
The U.S. Food and Drug Administration (FDA) notes that “the inherently small population of patients with a rare disease can also make conducting clinical trials difficult.”
Small and Dispersed Populations
With few eligible participants, trials cannot rely on large enrollment. Participants may live far from specialist sites, sometimes in other countries.
Variable Presentation
Symptoms and progression can vary widely between individuals with the same diagnosis. A single measure may not reflect what matters to every participant.
Assessments Designed for Other Populations
Many clinical assessments were developed in more common conditions or in adults. They may not capture the function or symptoms most relevant to a rare condition or to children.
Clinic Visits Capture Snapshots
A clinic assessment shows how someone performs on one day. In conditions where symptoms fluctuate, that day may not be typical.
Why Does Natural History Data Matter?
Natural history data describes how a disease behaves without treatment, and in many rare diseases that description is incomplete. FDA’s draft guidance Rare Diseases: Natural History Studies for Drug Development (March 2019) describes a natural history study as one that “collects information about the natural history of a disease in the absence of an intervention, from the disease’s onset until either its resolution or the individual’s death.” As a draft guidance, it contains nonbinding recommendations.
Baseline Characterization
Before a digital measure is used in an interventional trial, a team needs to know how it behaves in the population. That includes typical values, how much the measure varies from day to day and between participants, and how it differs across ages or disease stages.
What Families Can Sustain
Observational work also shows what wear schedules participants and caregivers can keep up over weeks or months. That is practical information a protocol team can use before committing to a design.
Where Digital Measures Fit
Wearable sensor data can be collected within observational studies, so that a candidate measure has baseline data behind it before an interventional trial begins. Whether that is the right approach depends on the condition, the concept of interest and the evidence the measure will eventually need.
What Can Digital Measures Add?
Digital measures collected through wearable sensors can capture information between visits, in the settings where participants live.
They can add:
- Data from daily life, not just from the clinic
- Repeated measurement over days and weeks, rather than one assessment per visit
- Information on characteristics a clinic test does not capture, such as sleep or nighttime breathing patterns
- Fewer demands on families when some measurement happens at home
Whether a specific digital measure adds value in a specific trial has to be shown. That depends on the measure, the population and the context of use, and it has to be supported by evidence.
The terms matter here. A digital biomarker is an objective, quantifiable physiological or behavioral measurement collected through a digital device. A digital endpoint is a trial outcome measure that uses one or more digital biomarkers to assess treatment effects, disease progression, or other study objectives. The distinction is set out in more detail in digital biomarkers vs digital endpoints.
How Can Patient and Caregiver Input Shape a Digital Measure?
Patient and caregiver input helps a team decide what to measure when published evidence about a rare condition is thin. FDA describes patient-focused drug development (PFDD) as “a systematic approach” to capturing patients’ experiences, perspectives, needs and priorities and incorporating them into drug development and evaluation. FDA’s PFDD methodological guidance series covers collecting patient experience data and using it in drug development.
Asking What Matters in Daily Life
People living with a rare condition, and the families who care for them, can describe which symptoms and limitations affect daily life most. That points a team toward a meaningful concept of interest before any device is chosen.
Caregivers as Observers
In pediatric conditions, or where participants cannot report symptoms themselves, caregivers often provide the day-to-day view. Their observations can help a team decide which aspects of movement, sleep or breathing a wearable sensor should capture.
Testing Feasibility With Families
Input is also practical. Families can say whether a device is comfortable, whether a wear schedule fits school and work, and what would lead them to stop wearing it. VivoSense has written about how patient-centricity in digital measure development has developed alongside regulatory guidance.
Why Algorithms Built on Healthy Adults Can Fall Short
Validation should always be evaluated within the intended context of use. A wearable sensor validated for healthy adults may not perform similarly in patients with neurological disorders, respiratory disease, or mobility limitations. That principle is part of how to select wearable sensors for a clinical trial.
In rare disease, that gap can be wide. Movement, gait, sleep and breathing patterns may differ substantially from the populations an algorithm was built on. A measure has to be developed and evaluated for the people it will be used with, which is the subject of validation evidence for digital measures.
Considerations for Pediatric Rare Disease Trials
Many rare diseases are diagnosed in childhood, which adds its own requirements for any digital measure.
Burden on Families
Parents and caregivers often manage wear, charging and troubleshooting. A wear schedule has to fit family life.
Wear Location and Comfort
Children may remove devices that are uncomfortable or visible. Device choice and wear location affect how much usable data a study collects.
Developmental Change
Children’s movement and sleep change as they grow. Measures and analysis plans need to account for normal development over the course of a study.
Can Some Measurement Move Closer to Home for Dispersed Families?
Some measurement can move closer to home, and for dispersed rare disease populations that can reduce travel for participants and caregivers. FDA’s final guidance Conducting Clinical Trials With Decentralized Elements (September 2024) states that “decentralized elements allow trial-related activities to occur remotely at locations convenient for trial participants.” The guidance carries nonbinding recommendations.
What Moves Home and What Stays in the Clinic
Wearable sensor measurement often suits the home, because it is designed to run during daily life. Other assessments may still need a site visit. A hybrid design keeps each assessment in the setting that suits it.
Operational Questions for Home Measurement
Devices have to reach families, families need clear instructions, and someone has to notice when a device stops recording. Wear compliance monitoring during the study lets a team follow up while there is still time to recover data. The operational side is set out in running decentralized clinical trials with wearable sensor data.
Which FDA Rare Disease Resources Should Sponsors Know About?
FDA’s Rare Diseases at FDA page lists several points of contact for the rare disease community. Sponsors planning a rare disease program can review them early.
- Office of Orphan Products Development: described by FDA as coordinating “FDA activities for rare diseases”
- FDA Rare Disease Innovation Hub: which FDA says “serves as a point of collaboration and connectivity between FDA’s medical products centers”
- Patient Listening Sessions: which FDA describes as “one of many ways the patient community can share their experience”
Regulatory expectations for novel measures continue to evolve, and requirements vary depending on the specific application. Early conversations can help sponsors understand what evidence a measure is likely to need.
Published Examples
VivoSense has published case studies on a rare condition, congenital myasthenic syndromes, and on a multi-site cystic fibrosis study.
Congenital Myasthenic Syndromes
A VivoSense case study, published in July 2026, describes a Phase 1b study evaluating ARGX-119 in 16 patients with congenital myasthenic syndromes (CMS), specifically the DOK7 variant. It describes “DHT-enabled gait analysis during the Six-Minute Walk Test (6MWT),” where DHT stands for digital health technology.
The case study reports “the clinically meaningful increase observed in Six-Minute Walk Test performance among ambulatory participants receiving ARGX-119.” It states that “Investigators observed improvements in both total walking distance and cadence,” that “VivoSense analyzed cadence patterns throughout the 6MWT to quantify fatigability,” and that “Researchers also observed strong correlations between in-clinic walking cadence and real-world mobility performance.” It also reports that “100% of participants reported a willingness to use digital health technology again in future clinical trials.”
These findings come from one early-phase study of ARGX-119, and the walk test results belong to that study. A correlation between clinic and real-world measures is not clinical validation. Read the ARGX-119 congenital myasthenic syndromes case study, and see how sensors extend the walk test in the six-minute walk test with wearable sensors.
Cystic Fibrosis
In a global cystic fibrosis study described in an April 2025 case study, VivoSense deployed sensor-based digital health technologies to monitor physical activity, sleep and cough across 200 devices at 18 sites worldwide. The study reported “99% data availability” and “94% wear compliance.” Read the cystic fibrosis case study.
How Do You Plan a Rare Disease Digital Measure?
Planning follows five stages, in order, starting from the clinical concept rather than the device.
Identify a Meaningful Clinical Concept
Start with what matters to patients and families living with the condition. Patient and caregiver input is especially valuable when published evidence is limited.
Select Appropriate Technologies
Choose devices suited to the disease state and the population of the patients, including age, mobility and what families can sustain.
Develop Digital Biomarkers
Build measures around how the condition presents, rather than applying defaults developed in other populations.
Establish Validation Evidence
Plan the evidence the measure needs for its role in the trial and its context of use. Small populations make early planning more important, not less.
Build Digital Endpoints
Define how measures will be analyzed and how they relate to the trial’s objectives.
A Planning Checklist for Rare Disease Digital Measures
Before a digital measure goes into a rare disease protocol, a team can work through these questions:
- Which aspect of daily life does the measure reflect, and have patients and caregivers confirmed it matters?
- What is known about the natural history of that aspect of the disease?
- Has the algorithm been evaluated in this population, including children if they will enroll?
- Which wear location and wear schedule can families sustain for the full study?
- Which assessments can happen at home, and which need a site visit?
- How will wear compliance be monitored, and who follows up when data stops arriving?
- What validation evidence does the measure need for its role in this trial?
- When will the team discuss the measure and its evidence plan with regulators?
Common Mistakes
Applying Algorithms From Other Populations Without Evaluation
Defaults developed in healthy adults may misread movement, sleep or breathing in a rare condition.
Choosing a Device Before Defining the Concept of Interest
A device chosen for its features may not measure what matters to patients with the condition.
Designing Wear Schedules Around the Protocol Instead of the Family
A schedule families cannot sustain can produce missing data.
Treating Correlation as Validation
Correlations between clinic and real-world measures are useful evidence. They do not establish clinical validity on their own.
Waiting to Discuss the Measure With Regulators
Novel measures can benefit from early discussion of evidence needs.
Rare Disease Digital Measures With VivoSense
VivoSense is a wearable sensor contract research organization (CRO) that works alongside the sponsor’s trial team and CRO on the digital measurement workstream. It helps choose the right device based on the disease state and the population of the patients, chooses what measures to capture, ships devices and trains sites, monitors real-time wear compliance, and delivers formatted regulatory-ready data packages for the study team.
VivoSense was founded in 2010.
Frequently Asked Questions
What counts as a rare disease in the United States?
The Orphan Drug Act defines a rare disease as a disease or condition that affects less than 200,000 people in the United States.
What makes rare disease clinical trials difficult?
Small, dispersed populations, variable presentation between individuals, limited natural history data and assessments that were often developed in other populations.
How are digital measures used in rare disease trials?
Wearable sensors can collect data between clinic visits, in daily life, on characteristics such as physical activity, gait, sleep and breathing patterns. Each measure needs evidence for its population and context of use.
Why does natural history data matter for a digital measure?
It shows how a measure behaves in the population without treatment, including typical values and day-to-day variation, which a team needs before using the measure in an interventional trial.
How do patients and caregivers shape digital measures?
They identify which symptoms and limitations matter most in daily life and test whether devices and wear schedules are feasible. FDA’s patient-focused drug development program describes approaches to capturing that input.
Can digital measures reduce the number of clinic visits?
Some measurement can happen at home, which can reduce the demands on participants and families. Whether a trial can reduce visits depends on its design and objectives.
Why do algorithms need to be adapted for rare disease populations?
Algorithms built on healthy adults may not perform similarly in populations with different movement, sleep or breathing patterns. Measures need evaluation in the population where they will be used.
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