Key Takeaway: By combining real-world data with robust analytical frameworks, we can advance precision medicine and personalized healthcare.

Photos: Reinhardt/Sommer.

- 📈Data-Driven Insights: Participants analyzed datasets from the Providemus project to understand how daily metrics inform individual-level healthcare decisions.

- 🤖 Model-Twin Randomization (MoTR): I introduced Eric J. Daza, DrPH, MPS’s method for AI-powered personalized analysis, showcasing its applicability in real-world scenarios.

- 🩺 Practical Application: Attendees learned to interpret wearable-derived metrics and translate them into actionable healthcare strategies.

- 🧠 Cognitive Performance Monitoring: We delved into how wearable and mobile technologies can passively assess cognitive performance, offering early insights into long-term cognitive/mental health trends.

I also shared recent findings from my PhD research at the University of Geneva, proving that everyday wearable and mobile technologies can passively estimate cognitive performance over the lifespan.

Below are some key points:

Today, I was honored to host a workshop illustrating how real-world longitudinal data—from wearables and other digital health technologies—can drive personalized medicine and research.

I also look forward to hearing different perspectives and discussing how technology is shaping the future of healthcare.
📅 Digital Health Innovation Forum | HPI, Potsdam, Germany | March 26–27, 2025
🔗 More details: hpi.de/en/hpi-digit...

... introduce Model-Twin Randomization (MoTR, by @ericjdaza.com ) as a method for AI-powered individual-level analysis, and guide a hands-on session where participants will explore real-world longitudinal health data.

The session will focus on how ubiquitous digital health data—from wearables and other smart technologies—can help advance personalized healthcare and research. I’ll share insights from my work on real-world digital health monitoring (the Providemus project at the University of Geneva), ...

I also look forward to hearing different perspectives and discussing how technology is shaping the future of healthcare.
📅 Digital Health Innovation Forum | HPI, Potsdam, Germany | March 26–27, 2025
🔗 More details: hpi.de/en/hpi-digit...

... introduce Model-Twin Randomization (MoTR, by Eric J. Daza, DrPH, MPS) as a method for AI-powered individual-level analysis, and guide a hands-on session where participants will explore real-world longitudinal health data.

The session will focus on how ubiquitous digital health data—from wearables and other smart technologies—can help advance personalized healthcare and research. I’ll share insights from my work on real-world digital health monitoring (the Providemus project at the University of Geneva), ...

🤝 Join the Conversation: I’m eager to hear your thoughts! Whether you're in computer science, psychology, or medicine, your insights could be invaluable. Feel free to discuss this below or message me directly.

Our ultimate goal is to develop more accurate and interpretable models for detecting fluctuations in cognitive performance and early signs of decline.

🔭 What's Next? This poster and our earlier work will form the foundation of my upcoming publications. Next steps:
- Investigating how and why these predictive models succeed or fall short.
- Applying cutting-edge machine and deep learning methods to exploit our dataset fully.

- Interdisciplinary Applications: Offering valuable insights for computer science, psychology, medicine, and more.