One of the things that I’ve previously noted is that I’ve seen a fair amount of variability in using Fiasp, both in insulin sensitivity and in mealtime sensitivity, best described by Carb Ratio.
What I’d felt particularly was that during the week, I was needing substantially more insulin for food than at the weekends, but I hadn’t done anything to really check this.
As I’ve mentioned previously, one of the huge benefits of having the various DIY Loop set-ups is that you have a great deal of instrumentation easily available and this allows you to understand quite a lot about what’s going on with your body/diabetes/insert what you want to know about here.
So coming back to the earlier point, I have two mechanisms for understanding variability in Carb Ratio. I can either take it from the daily Autotune run, or extract the data from NightScout and manipulate it. The latter should broadly match with the former.
If we take a month of data then, what do we see? The below:
What’s rather disconcerting about this is that it is very up and down, with some days showing very low ratios and others very high. There is, however, almost a pattern to be seen, with weekends tending to look like they have more carbs per unit of insulin compared to the weekdays. In the graph above, the Orange line is the average.
If we follow through on the above mentioned hypothesis, that weekends require less insulin per gram of carbs than weekdays, it would make sense to do a clustered view of the data using each weekday as a node, and potentially weekdays and weekends as additional nodes.
This gives us the below:
Again, the orange line is the average carb ratio. This identifies a very clear pattern, which is that Saturday and Sunday appear to require less insulin for food than most weekdays, with Monday and Tuesday need ing less than the second half of the week.
Comparing the average weekend day to the average weekday day, we see that the difference is roughly 12.2g/iu to 9.5g/iu. Now you may ask whether there is a correlation between the amount of carbs that I eat per day of the week and the variation in carb ratio for that day, in that on those days where more carbs are eaten, the g per insulin unit becomes lower. What is worth noting is that the standard deviation of the data on a daily basis can be quite high, so it’s not a perfect analysis.
The relationship isn’t particularly strong in that respect and whilst there is a correlation it is a negative correlation, and not a very strong one, with a value of -0.16. So it would appear that on days where I require less insulin per gram of carbs, I eat more carbs, which makes sense, as I am likely to be avoiding hypos. What I also note is that carb consumption has a standard deviation of about 12%.
What can we take from this? Well, it’s clearly not all about the Fiasp. Whilst I may have noticed more variability on Fiasp, there is clearly something additional going on.
Based on the rather striking visual above, what I take from this is that a combination of tiredness, stress and sedentariness play a part in how my body interacts with insulin. Whilst I may walk 10,000 steps most days, weekdays sees this generally split into two periods as I am on my way into work and on my way home. I am then sedentary throughout the day, whereas at the weekends, the steps are more evenly distributed throughout the day, and I am doing more activities, instead of sitting at a desk.
Secondly, as the week progresses, it’s fair to say that job stress is definitely higher than when not working, until late on Friday, when I have a weekend to recover. So stress hormones will be playing a part.
Finally, weekday sleeping patterns are constant, but rarely exceed five hours, whereas weekend evenings (Fri-Sun) generally result in six to eight hours of sleep. it appears this benefit is carried over into the early part of the week.
For me, in my n=1 sample, it would appear that during the week, I’d benefit from more sleep and managing stress slightly better. What it also shows is that while we may attribute variation to something like the insulin we use, there are often other factors at play, and we do need to consider those when looking at the overall picture.
Finally, it adds to the questions we need to ask about how we go about calculating the various numbers that we use to live by. Clearly, from my perspective, I’ve been seeing a lot of variation, and it’s driven by a number of factors, but it also suggests that those days where we just can’t seem to avoid hypos are often driven by physiological factors. Remember that many pumpers use a different basal pattern at weekends? Well maybe they should also use a different ISF and Carb Ratio as well. For some, it may even suggest that daily ones make sense!
The more instrumentation we get of the data that we can produce the more that T1D strikes me as being similar to a modern fighter jet. Inherently unstable, the pilot (us) is asked to fly it with the aid of multiple tools. The better the tools and understanding of how to use them are, the more stable the flight…