Transforming Sleep Assessment in Clinical Trials with Wearables and Digital Endpoints

Sleep studies are evolving from single-night, lab-based assessments to continuous, real-world monitoring powered by wearable technology. By combining polysomnography with validated digital endpoints, clinical trials can achieve a more complete, longitudinal understanding of sleep and its impact on health outcomes. Empatica's clinical-grade wearables and platform are designed to enable this hybrid approach, extending sleep measurement beyond the lab into real-world settings.

What is a sleep study and how does it work?

A sleep study, also known as polysomnography (PSG), is a comprehensive diagnostic test used to evaluate sleep disorders. Conducted in clinical or laboratory settings, PSG records multiple physiological signals during sleep, including brain activity (EEG), eye movements (EOG), muscle activity (EMG), heart rate and rhythm (ECG), respiratory effort and airflow and oxygen saturation (SpO2). These measurements enable clinicians and researchers to diagnose conditions such as obstructive sleep apnea (OSA), narcolepsy, insomnia, and REM sleep behavior disorder. While PSG provides highly detailed insights into sleep architecture, it is typically limited to one or a few nights in a controlled environment.

Why wearable sleep monitoring matters in clinical trials

Wearable technologies enable a shift from episodic to continuous sleep measurement, supporting more patient-centric and scalable trial designs. Key advantages include:

Continuous monitoring

Capture sleep over days or weeks, not just single nights

Real-world relevance

Measure sleep in natural environments

Improved sensitivity to change

Capture longitudinal trends and subtle treatment effects over time

Reduced patient burden

Enable remote and decentralized trials

Enhanced endpoint quality

Provide objective, high-frequency data

Core digital sleep endpoints and biomarkers

Digital endpoints derived from wearable devices provide objective measures of sleep quantity, quality, and related physiological processes. Empatica’s clinical-grade wearables and platform support the collection of a broad set of validated sleep and physiological biomarkers, enabling consistent, high-quality endpoint generation in clinical trials. Key sleep metrics include:

Total sleep time (TST)
Sleep efficiency
Sleep onset latency (SOL)
Wake after sleep onset (WASO)
Sleep fragmentation index (SFI)
Sleep interruptions and wake bouts
Circadian rhythm stability
Time in bed (start/end)

Download full list of digital biomarkers

Clinical-grade vs. consumer sleep tracking devices

Not all wearable devices are suitable for clinical research.

Clinical-grade

Clinical-grade devices, such as those used in research settings, are characterized by:

FDA clearance or CE marking
Robust validation studies
Reliable and reproducible measurements
Data quality suitable for regulatory submissions

Consumer-grade

Consumer-grade devices or fitness trackers are designed for general wellness and often lack key requirements for clinical research:

No regulatory clearance (e.g., not FDA-cleared or CE-marked)
No rigorous validation against polysomnography (PSG)
Limited transparency in algorithms and data processing

Sleep as an endpoint across therapeutic areas in clinical trials

Sleep is increasingly recognized as a clinically meaningful endpoint across multiple therapeutic areas, not only in primary sleep disorders but also in conditions where sleep disruption affects symptoms, functioning, treatment response, or quality of life. In clinical trials, sleep can be used as a primary, secondary, or exploratory endpoint depending on the indication, mechanism of action, and study design.

Oncology

Sleep disruption is highly prevalent in oncology and may be influenced by pain, fatigue, anxiety, treatment burden, corticosteroids, hospitalization, and circadian disruption. Poor sleep can affect daily functioning, emotional well-being, and perceived quality of life throughout treatment.
In oncology trials, sleep may be relevant as:

A tolerability endpoint during treatment
A quality-of-life endpoint alongside fatigue and symptom burden
An exploratory endpoint in supportive care or survivorship studies
A marker of treatment-related change over time

Wearable monitoring can help quantify sleep patterns longitudinally and complement electronic clinical outcome assessments (eCOA), especially when subjective sleep quality and objective sleep continuity do not fully align.

Neurology

Sleep is deeply intertwined with neurological disease and may reflect both disease pathology and treatment effects. Sleep disturbances are common in conditions such as Parkinson’s disease, epilepsy, stroke recovery, neurodegenerative disorders, and REM sleep behavior disorder.
In neurology trials, sleep can be relevant as:

A disease-related outcome
A marker of progression or symptom burden
A treatment response indicator
A complementary endpoint linked to motor, cognitive, or autonomic outcomes

Continuous wearable monitoring is especially useful in neurology because symptoms often fluctuate over time, and sleep disturbances may worsen before they are clearly captured during site visits.

Psychiatry and mental health

Sleep is a central dimension of psychiatric and behavioral health. Disturbances in sleep are closely associated with depression, anxiety, stress, circadian dysregulation, and broader changes in mood and functioning.

In psychiatry trials, sleep may be relevant as:

A direct symptom domain
A treatment response marker
A relapse or deterioration signal
A bridge between subjective well-being and objective physiological change

Wearable-derived sleep data can help quantify patterns that patients may not accurately recall, including fragmented sleep, delayed sleep timing, or variability across nights.

Metabolic and obesity-related research

Sleep is increasingly studied in obesity, metabolic dysfunction, and cardiometabolic health because it is linked to energy balance, physical activity, glucose regulation, and behavioral health. In these studies, sleep can function as both a clinical outcome and a contextual variable that influences treatment response.

In obesity and metabolic trials, sleep may be relevant as:

A behavioral health endpoint
A contributor to weight-management outcomes
A contextual factor for interpreting physical activity and recovery
An exploratory marker in lifestyle and intervention studies

Cross-therapeutic value of objective sleep monitoring

Across therapeutic areas, objective sleep monitoring offers several advantages in clinical trials:

Capture real-world sleep outside the clinic or sleep lab
Enable repeated, longitudinal measurement with lower patient burden
Enrich eCOA data with digital measures to identify gaps between patient-reported and objectively measured sleep
Support multimodal endpoint strategies by linking sleep with activity, stress, autonomic signals, and quality of life

Enabling scalable sleep monitoring in clinical trials

Empatica provides clinical-grade wearables and platform designed to support sleep monitoring in clinical research. By combining wearable monitoring with traditional sleep studies, sponsors can achieve a more complete and scalable approach to sleep assessment.

Key capabilities include:

Continuous actigraphy-based sleep monitoring
Longitudinal data collection in real-world settings
High patient adherence through wearable design
Integration with clinical trial workflows
Secure, compliant data infrastructure (GDPR, HIPAA)

See how Empatica’s wearable-derived sleep data is validated and used in clinical research.

See publications

Additional Resources

FDA Clears Empatica’s EmbraceMini for Sleep Monitoring

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Empatica unveils EmbraceMini, the world’s smallest actigraphy wearable for clinical trials

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Empatica Actigraphy

SOLUTION

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Empatica joins Sleep Revolution, aiming to diagnose and treat OSA

CASE STUDY

EmbraceMini Wearable

COMPONENT

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Frequently Asked Questions

Can wearable devices be used as endpoints in clinical trials?

Yes. Wearable-derived sleep metrics can be used as primary, secondary, or exploratory endpoints depending on study design and regulatory strategy. In many trials, they are used to complement clinical scales and patient-reported outcomes by providing objective, high-frequency data.

How does actigraphy compare to polysomnography in clinical research?

Actigraphy and polysomnography (PSG) are complementary. PSG provides detailed sleep architecture in controlled settings, while actigraphy enables continuous, real-world monitoring over extended periods. Together, they offer both depth (PSG) and longitudinal context (wearables).

What is WASO and why is it important in clinical trials?

Wake After Sleep Onset (WASO) measures the total time spent awake after initially falling asleep. In clinical trials, WASO is a key indicator of sleep fragmentation and is widely used to assess treatment effects on sleep continuity across conditions such as insomnia, oncology-related fatigue, and obstructive sleep apnea.

How accurate are clinical-grade wearable sleep devices?

Clinical-grade wearable devices show strong agreement with PSG for key metrics such as total sleep time and sleep efficiency when validated using rigorous methods (e.g., epoch-by-epoch comparison). Accuracy varies by endpoint, and devices are typically used to complement, not replace, PSG.

Can wearable sleep data replace patient-reported outcomes?

No. Wearable data does not replace patient-reported outcomes (ePRO/eCOA) but complements them. Combining objective sleep metrics with subjective reports provides a more complete view of patient experience, especially when discrepancies between perceived and measured sleep are clinically relevant.

In which therapeutic areas is sleep used as an endpoint?

Sleep is used across multiple therapeutic areas, including oncology, neurology, psychiatry, and metabolic research. It can reflect disease burden, treatment tolerability, and quality of life, and is often included as a secondary or exploratory endpoint.

What are the advantages of continuous sleep monitoring in decentralized trials?

Continuous wearable monitoring enables remote, real-world data collection with lower patient burden. This supports decentralized and hybrid trial designs, improves retention, and increases sensitivity to change by capturing longitudinal sleep patterns rather than single-night snapshots.