#freestylelibre – Closing the feedback loop

#freestylelibre – Closing the feedback loop
#freestylelibre – Closing the feedback loop

Up until recently, I’d been putting my sensor in 24 hours ahead of when I needed it to give my body a chance to chill it out and for both of us to get used to each other, mostly based on the feedback from the various groups that had seen the early measurements end up all over the place and therefore wasting £3.57 of the sensor cost on a 24 hour period where it wasn’t really much use.

At my next change I managed to forget to bring the spare with me to work, so I had eighteen hours without a sensor. First up – how did it feel?

It was an odd sensation. I was no longer beholden to the graph. I couldn’t tell at an instant what might be happening and imagine what might happen next! In many ways, it was liberating. And then reality kicked in. I had to finger prick, and instead of being presented with a wealth of data on which to make a decision I had a single, point in time data point with no other information. And I had to make a decision on that. Incredibly frustrating.

But what did this demonstrate? It confirmed what I have considered to be the issue for a long time. That the best decisions on glucose management come with far more data than a finger prick can provide, namely, in what direction is your level going, and how fast.

With these three pieces of data, it is much easier to manage levels and get more control. The traditional single prick is a vastly superior model to urine testing, but tells you a point in time datapoint. In order to make a decision, you have to follow it with another fingerprick, giving you trajectory and velocity.

With Libre and CGM, you get that when you check the reader. You already have trajectory, velocity and current value, therefore what you decide to do next is much more informed.

Admittedly, there is still an issue that it is a point in time snapshot you are checking, so what happens next might not be what you expect. Taking everything into account, which includes what insulin you have taken up to that point and when, and what food you have eaten, the continuous historic data provides a far more useful data set to inform you about your current state.

While there may be issues with the sensors reading high or low, as long as you know what that difference is and the trend data is correct, you still have that added data to make a more informed decision.

I think this is really where the strength in this product lies. If you are very active, very brittle or simply try and eat a normal NIC eatwell plate, it enables you to have a much more informed view of what is going on with your body.

Finally glucose monitoring has an affordable way of closing the feedback loop.

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