ADAMMA - Core for AI & Digital Biomarker Research

Research

Our group focuses on advancing digital biomarkers—objective, quantifiable physiological and behavioral data collected through digital devices. These biomarkers enable continuous, non-invasive disease monitoring at a fraction of the cost of traditional episodic follow-ups and clinic visits. By frequently tracking disease progression over extended periods, digital biomarkers support proactive care, enabling timely adjustments to treatments and medications while reducing the need for in-person appointments. Additionally, these technologies are integral to hybrid and fully decentralized clinical trials, offering the ability to capture surrogate endpoints and provide critical insights into the efficacy and effectiveness of investigational therapies.

The Anatomy of Digital Biomarkers.
The Anatomy of Digital Biomarkers.

Projects

Digital Biomarker of Aging
Digital Biomarker of Aging

This project aims to develop a scalable and accessible method for estimating biological age using lifestyle behaviors captured by digital devices such as smartwatches and wearables. Lifestyle behaviors are closely linked to our health and aging, and by analyzing continuously collected accelerometer data to quantify physical activity, circadian rhythms, sleep patterns, and movement behaviors, we can gain insights into how these factors influence biological aging.

Alex - Design, Development and Evaluation of a Digital Health Assistant for Paediatric Asthma
Alex - Design, Development and Evaluation of a Digital Health Assistant for Paediatric Asthma

This project aims to create an open-source smartphone-based digital health assistant (DHA) for remote disease monitoring and personalized patient coaching. The project will assess the feasibility and scalability of the DHA “Alex” for pediatric asthma monitoring in the socio-economic setting characteristic of low- and middle-income countries.

CLAID - Closing the Loop on AI Data Collection
CLAID - Closing the Loop on AI & Data Collection

CLAID is an open-source initiative to develop, validate and share AI models, digital biomarkers and healthcare applications. Our goal is to transfer research findings from the lab into the real-world. We invite researchers, clinicians and developers to use and contribute packages and to participate in our community.

Comparative Efficacy of Commercial Wearables for Circadian Rhythm Home Monitoring from Activity, Heart Rate, and Core Body Temperature
Comparative Efficacy of Commercial Wearables for Circadian Rhythm Home Monitoring from Activity, Heart Rate, and Core Body Temperature

While many commercial wearables are available for circadian rhythm monitoring, their efficacy remains underexplored. By conducting a two-week longitudinal observational study, we validated two commercial devices, finding they outperformed research-grade tools for circadian rhythm monitoring.

Noninvasive, Multi-modal Biomarker of Systemic Inflammation
Noninvasive, Multi-modal Biomarker of Systemic Inflammation

This project explores the digital potential of assessing inflammatory biomarkers, such as C-reactive protein (CRP), in systemic inflammation. Currently, CRP measurement relies on invasive blood tests, and no noninvasive, digital methods exist for continuous remote monitoring. Developing a noninvasive, digital CRP biomarker offers unprecedented potential for remote monitoring, early detection of exacerbations, and timely intervention.

Trends in voice characteristics in patients with heart failure
Trends in voice characteristics in patients with heart failure

The sensitivity of voice to fluid accumulation inspires the development of a novel monitoring tool for heart failure self-management. This longitudinal study aims to predict the health status of stable heart failure patients using audio data collected through mobile devices.

Smartphone-Based Cough Detection
Smartphone-Based Cough Detection

This project aims to develop a cutting-edge smartphone-based cough monitoring system designed to detect, record, and analyze cough patterns in real-time. By leveraging advanced algorithms and the smartphone’s built-in sensors, the system will provide an accessible, non-invasive tool for continuous respiratory health monitoring.