Digital Health

Digital Health

Early SignalEarly Signal (EaSi) is CVB’s digital health platform designed to amplify the range of information collected from wearables and other sensors, to improve the well-being of patients, as well as others experiencing the ill effects of deficient or disturbed sleep. The frequent, and sometimes ongoing, behavioral, cognitive, physiological, and environmental data, and the algorithms they enable, can be used to quantify brain health and disease. This will improve clinicians’ ability to diagnose and understand a patient’s unique presentation of disease and to tailor treatment accordingly. The EaSi device lab, Technical Performance Verification Study, and Trial-in-a-Box platform, described below, are just three of CVB’s EaSi projects that represent a giant leap forward toward this brain health goal.

Device Lab

The device lab rigorously evaluates both consumer and research devices to assess their sensitivity, specificity, accuracy, and precision in measuring what they purport to measure (e.g., movement, heart rate, sleep stages). Based on the user’s needs, they then make recommendations for use based on the accuracy, availability of raw data, derivable metrics, and work required to access them, or non-transparent metrics.

Technical Performance Verification (TPV) Study

Sleep disturbance is common in individuals with post-traumatic stress

disorder (PTSD) and Traumatic Brain Injury (TBI), as well as many other conditions. The gold standard assessment for sleep-wake disturbances is the overnight polysomnogram (PSG), but in-lab PSGs are cumbersome and resource intensive, and they don’t always accurately reflect a night’s sleep in a person’s home environment. However, there is a growing availability of wearable and home sensor devices that can assess sleep-wake patterns in a more efficient and cost-effective way that reflects how the person sleeps at home.

"Research has shown that disruptions in sleep are one of the first things to happen when an increase in stress occurs"

The goal of the Technical Performance Verification (TPV) study is to directly compare some of these biosensors with PSG, to assess their accuracy in a relevant population of subjects with insomnia and PTSD. As insomnia and PTSD are commonly comorbid, the TPV will be able to assess the devices for their ability to accurately measure sleep patterns in those who might have more nightmares, limb movements, and heart rate and respiration rate changes. We test these devices—both research grade and consumer grade—to evaluate whether they measure what they are intended to measure, e.g., an accurate step count, and whether they provide the information needed to calculate a person’s sleep pattern.

Based on device lab results, for our first study, we selected the six most promising sleep-related devices and launched the TPV study with our partner, a leading sleep research team at the University of Pennsylvania. The TPV study results will be instrumental in developing an improved sleep solution for this population or for creating an objective endpoint in therapeutic clinical trials. Based on the TPV results, EaSi will identify which device, or device combination, to integrate with other typical sleep assessments in a specific context. CVB’s next study will use the recommended devices and algorithms to create a closed-loop system for treating clinicians that visualizes the device data on a clinically relevant dashboard. Clinicians will be able to use this information to guide their treatment decisions, enabling best practices for personalizing treatment and effectively restoring sleep and function.


Trial-in-a-Box is a fully automated clinical trial solution for the collection, processing, and analysis of digital health data in traditional, hybrid, and fully decentralized trial formats. In the fully decentralized setting, this allows sponsors to send wearable and home sensor technology study kits directly to eligible study participants without requiring them to visit a study center. This allows for participation of a more diverse and often less-studied population.

In all trial formats, Trial-in-a-Box provides automated processing of each digital data type, automated analysis pipelines, automated clinically relevant visualizations, and the possibility of identifying digital biomarkers of disease, including diagnosis, prognosis, and predictors or indicators of response to treatment. The devices are paired with a downloadable study app on the participant’s own device, to collect related subjective outcomes such as traditional scales of depression, function, and activity, as well ecological momentary assessments.

CVB is investigating the feasibility of the Trial-in-a-Box platform through its Sleep PoC study. The fully decentralized trial employs two wearable and home sensor devices vetted through the device lab for collection of sleep-related metrics, as well as a study app, to assess sleep patterns and subjective outcomes, respectively, in participants with insomnia over a two-week period.