Implementing a DIY Closed-Loop Diabetes Management System (Trio + Loop Follow)

Byadmin

When my daughter was diagnosed with Type 1 diabetes in May 2024, we were suddenly immersed in a world of 24/7 care, critical decisions, and little margin for error. While still in the hospital, I began researching how to best support her—starting with a custom Google Sheet to calculate insulin doses and track meals, blood glucose readings, and insulin doses.

A screenshot from the Google Sheet I create to track carbs and calculate insulin boluses.

I immersed myself in books, forums, YouTube videos, and podcasts, recognizing that managing Type 1 diabetes requires deep understanding to make sound therapy decisions as part of daily life. The complexity is extraordinary, and the learning curve steep.

That’s when I discovered “Automated Insulin Delivery: How artificial pancreas “closed loop” systems can aid you in living with diabetes” by Dana M. Lewis and her blog. Reading how she and the community had developed and tested these open-source tools—with rigorous research and proven real-world outcomes—gave me confidence to proceed. This wasn’t just “something from the internet,” but a robust, evidence-based system grounded in both engineering excellence and lived experience.

Within two months of diagnosis, I had:

  • Deployed Nightscout (via Nightscout Pro) for centralized data visualization and remote monitoring
  • Built and deployed Trio using GitHub and Apple’s TestFlight, navigating the complete workflow from code branching to app provisioning and installation
  • Configured personalized therapy settings including ISF, I:C ratios, basal profiles, UAM, and SMB parameters
  • Implemented Loop Follow on my device for real-time glucose monitoring and alerts

Even before she received her insulin pump, we used Trio to log carbs and boluses into Nightscout—allowing us to master the interface and build confidence in the system. When we transitioned to automated insulin delivery, the learning curve felt manageable.

Technical Implementation

  • GitHub workflows, Apple Developer Program, TestFlight deployment
  • Nightscout cloud platform + Loop Follow integration
  • Open-source diabetes technology: Trio (oref1 algorithm)
  • Health data analysis and therapy optimization
  • Self-directed technical learning under real-world pressure

Impact

  • Reduced manual intervention through intelligent automation
  • Enabled 24/7 remote monitoring and data-driven therapy adjustments
  • Improved clinical outcomes

Community Gratitude

Managing Type 1 diabetes means making dozens of therapy decisions daily—with no breaks, no holidays, and low tolerance for error when health and quality of life are at stake. It demands constant vigilance and emotional resilience from both patients and caregivers.

I’m profoundly grateful to the open-source diabetes community (#WeAreNotWaiting) — developers, researchers, testers, and fellow T1D parents and patients—who share their knowledge freely and built systems like Trio, Nightscout, and Loop Follow. Their dedication and generosity make safer, more confident care decisions possible for families like ours.

Resources and Acknowledgments