Decentralized trials platform

Epilepsy Monitoring

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Decentralized trials platform | Empatica

Designed for the patient

All data is collected via EmbracePlus, a comfortable, award-winning medical wearable, designed to look like a consumer smartwatch that can be worn all-day long. EmbracePlus is waterproof, with a breathable, hypoallergenic silicone band and an E ink display to check the time and battery levels
Data is streamed via a smartphone app that is simple to use and install on popular smartphone models. The app is translated into over 40 languages, with more continuously being added, allowing you to enhance diversity in your research without disadvantaging participants
Setup is fast and easy, and can be done autonomously with the support of dedicated patient materials
Battery life can last up to 28 days, reducing charging time

Made to support global trial operations

Remote and secure enrollment, participant, and study management through the Care Portal
Data collection and streaming to the Cloud is continuous and automated, thanks to the EmbracePlus capabilities
Continuous support by a dedicated team, help desk, and materials, covering site and patient needs across every step of your study
Empatica’s experienced team handles all logistics, including device shipping, replacement, and returns
Sites receive frequent adherence reports via email, so that staff can quickly intervene when they identify compliance issues
Regulatory support and audit trails, including IRB/Ethics committee-friendly materials
Empatica performs frequent testing and certification of new phone models, and provides provisioned phones, to ensure technological compatibility

Enhanced for the scientist

Purpose-made for remote data collection, the Empatica Health Monitoring Platform is FDA-cleared and developed to provide researchers with medical-grade data, collected continuously from participants’ homes
Accelerate treatment research with over 300 clinically validated and research-grade digital biomarkers that can be used as digital endpoints in your trials
Develop your own digital biomarkers using high-frequency raw data from the EmbracePlus sensors
Visualize digital biomarkers and patient data inside the Care Portal, spotting patterns and adverse events

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.

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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.

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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.

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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.

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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.

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