Digital Biomarker Development with Smart Omix
Leverage our expertise to build validated, clinically relevant digital biomarkers.
Join researchers developing digital biomarkers using next generation clinical research technology.
What is a digital biomarker?
Digital biomarkers are patient generated physiological and behavioral measures that are collected through connected digital devices. The collected data is then used to explain, influence or predict health related outcomes of an individual.
The rising importance of digital biomarkers for research
Capture unmet patient need for regulatory approval with high-density data and digital biomarkers
Incorporate wearables, smartphones and connected devices into clinical trials to better capture real-world lived experiences longitudinally.
Accelerate label expansion, exploration for new indications and the development of Software as a Medical Device (SaMD) with predictive or diagnostic capabilities.
Inform disease management, commercialization efforts and therapeutic choice
Monitor real-world device, drug or intervention efficacy
Build objective measurement of patient centered outcomes for insurance reimbursement
Developing digital biomarkers with Smart Omix
Collect rich patient-reported data: e.g. surveys, photos etc.
Use neural nets to augment engagement and embed AI into your data collection.
Layer participant-reported data with granular, validated patient-generated health data like sleep, activity, vital signs and location.
Leverage our team’s expertise to analyze data and build a model
Clinically and analytically validate digital biomarkers.
Digital biomarker development in action
A decentralized, prospective observational study to collect RWD from patients with myasthenia gravis using smartphones
UCB and Sharecare report results from a 3-month prospective observational study in adults with myasthenia gravis (MG) using fully decentralized methods at MGFA 2022.
Predicting Environmental Allergies from Real-World Data
Using Smart Omix, a proprietary digital clinical research platform powering mobile research studies, the Sharecare team developed and trained a machine-learning algorithm to predict the emergence and severity of symptoms related to allergic rhinitis.