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Empatica Health Monitoring Platform for Clinical Trials

Empowering clinical research with high-precision digital measures

Enhance your clinical trials with raw data and digital biomarkers of the highest quality, with an easy-to-use, full-stack platform designed to reduce patient and site burden. Conducting research at scale from patients’ homes has never been easier.

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Scale your research across studies and therapeutic areas with a single platform

Scalable

Scalable across studies and geographies, with simultaneous data collection from thousands of devices and multiple language support

Modular

One multi-use, adaptable wearable that can be worn across different areas of the body depending on your study needs

Reliable

Secure and compliant FDA-cleared technology and dozens of accurate digital measures, for guaranteed quality data

<2 months

from contract signing to starting data collection

>90% compliance

with EmbracePlus used as an actigraphy sensor¹

120+ Industry sponsored trials

have already implemented our Platform to collect data

1. Feasibility of implementing wearable technologies for in-home assessment of behavioral and event-related potential responses after sleep and wakefulness in Alzheimer disease

Achieve multifaceted trial outcomes with double-device monitoring

Monitor each participant using 2x devices, in any form factor of your choice. Data flows into a single app installed on participants’ phones, ensuring the same, low-burden experience.

Build complementary datasets

Track vitals and other specialized digital biomarkers alongside actigraphy data, with 300+ digital measures to choose from.

Discover correlations

Reveal important correlations that single-device monitoring can miss with accurate and in-sync time-stamping.

Achieve more accuracy and versatility

Combine data collected from different body parts, such as lumbar and waist monitoring with wrist data.

Design more sophisticated protocols

Leverage the ability to capture different types of digital endpoints simultaneously, and demonstrate the reliability of your study.

Streamline clinical trials with Empatica’s Cloud API

Seamlessly integrate the Empatica Health Monitoring Platform into your existing clinical trial management system (CTMS) with our Cloud APIs.

Reduce study complexities with seamless data flow
Leverage Empatica’s 300+ digital measures and FDA-cleared medical wearable
Manage EmbracePlus users directly from your own software

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Empatica Actigraphy

Explore our full suite of activity-based digital biomarkers, available with our Actigraphy sensor modes

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Regulatory and Compliance

Empatica’s constant and decisive efforts aim at ensuring the safety, effectiveness, and quality of its medical devices as well as providing products that meet customer and applicable statutory and regulatory requirements

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Thousands of papers have been published using our technology

We collaborate with the best research teams in the world to advance the understanding of human health in real-life settings. Our work is focused on measuring and quantifying the impact of wearable technology in health, while our products have been used in a breadth of contexts, from large-scale clinical trials to intimate academic studies.

Detecting Moments of Stress from Measurements of Wearable Physiological Sensors

This research aims to bridge the gap between laboratory settings and real-world field studies by introducing a new algorithm that leverages the capabilities of wearable physiological sensors to detect moments of stress (MOS).
Results: our results overall demonstrate that stress can be reliably detected with an accuracy that varies from 75 to 100%, with an average of 84%. [...] the defined critical values are tailored to the current research, they do not serve as global values for stress detection.

Using wearable technology to detect prescription opioid self-administration

The current work examined the use of a wearable sensor to detect self-administration of opioids after dental surgery using machine learning.
Results: results from training and testing model of machine learning indicated that opioid self-administration could be identified with reasonable accuracy, leading to considerable possibilities of the use of wearable technology to advance prevention and treatment.

Engineering digital biomarkers of interstitial glucose from noninvasive smartwatches

This study outlines a novel machine learning model which predicts interstitial glucose levels using non-invasive measurements with impressive accuracy.
Results: the study demonstrated the feasibility of using noninvasive and widely accessible methods, including smartwatches and food logs recorded over 10 days, to continuously detect personalized glucose deviations and to predict the exact interstitial glucose value in real time with up to 84% and 87% accuracy, respectively.

Integrating a Mobile Health Device Into a Community Youth Mental Health Team to Manage Severe Mental Illness: Protocol for a Randomized Controlled Trial

The aim of this study is to examine (1) whether an mHealth device integrated into a community mental health team can improve outcomes for young adults with severe mental illness and (2) whether the device detects periods of mental health versus deterioration.
Results: coming at the end of the study.

Ictal autonomic activity recorded via wearable-sensors plus machine learning can discriminate epileptic and psychogenic nonepileptic seizures

The study demonstrates that epilpetic and psychogrenic non-epileptic seizures can be discriminated based on signals related to autonomic nervous system activity recorded via wearable sensors.
Results: classification performance achieved a predictive accuracy of 78 ± 1.5% on previously unseen data for whether a seizure was epileptic or psychogenic.

View our publications