I’ve written in the past about “Streaking“, and how expectations of getting 100% Time-In-Range can be unreasonable. Any of us can be within range for 5 minutes, but the longer and longer we measure the harder and harder it gets to not cross either of those borders. Unless the range is unreasonably large of course. …
In the last article I discussed what I think is a remarkable comparison between my recent personal data and the output of a 2019 CGM study of people without diabetes. At the end I showed a graph stretching over 20 years. It shows my HbA1c results over that time, overlaid with the TIR data from …
Further comparison with HCL and non-diabetic data Read More »
I’ve written multiple times about the benefits of using different ranges for Time In Range (see “Targets and Goals“). This is revisiting that topic in light of some recent experimental data. Like many this article has become quite long, but it’s split into sections: Terminology introduction, and choosing a new range based on published study …
I’ve done articles like this in the past, comparing my clinical results over the years. But I’ve just hit what feels like another milestone, so I’m going to re-do it using my current reporting tools. I’m going to show data from four points in time: 2017, when I’d only recently started using an Automated Insulin …
Progressing through Hybrid and “Fully” Closed-looping Read More »
I recently wrote about the value of HbA1c in measuring more than just our average BG level. And in fact that it’s often not accurate in that. Our best tools for measuring our BG patterns at the moment are Continuous Glucose Monitoring (CGM) systems. Basic data CGM data can show us is how much our …
Last month I mentioned that I had modified the parameters I was using for the insulin activity/IOB modelling in my AID/loop system. Specifically, for Lyumjev and for Humalog I’m now using 9 hours in the exponential IOB model. I showed statistics from two 4-week samples of Lyumjev, and then a 7-day sample using this timing …