The below is a list of foods I commonly eat that I decided to analyse. I have listed some lower-carb high protein meals, snacks and a few higher-carb meals. These are all more regular meals so I have had the opportunity to adjust my insulin strategy to better cover the various aspects of the meal.
Part of my strategy is to adjust the insulin timing (pre-bolus), starting BG (using temp targets or exercise) and the amounts of insulin used. The amount of insulin used needs to be adjusted based on the macronutrient composition, the amount of fibre and the amount of sugar alcohol in your meal. Sugar alcohols are often not listed on food labels in Australis and those foods should be avoided. Read my post on injecting for protein and fat for a better understanding of how your insulin dose will need to be adjusted to compensate for protein and fat.
To analyse a meal I use three (3) values, standard deviation, time in range (TIR) and Coefficient of the variation. These three (3) values will assist you in determining how good or bad a meal was for you in terms of blood sugar impact (BGI). You can also use the difference between the 3 hour and 5 hour standard deviations to ascertain if you are covering gluconeogenesis correctly.
Time in Range (TIR): For TIR we are looking for a high percentage of your readings within a normal (I use 3.9-7.8 mmol/l ) range.
Standard Deviation: For standard deviation I look for values under 1 as a meal that has little to no blood glucose impact (BGI).
Coefficient of the variation (CV): Is the standard deviation divided by the average glucose. Its a measure that helps normalise the results by reducing the influence on average glucose. Most studies indicate that anything under 33% is good. The lower the number the better the outcome.
On the 04 February 2022 I decided to switch from FreeAPS (Loop) over to AAPS. At that point I had been successfully looping for 6 months with FreeAPS (Loop) but I wanted to get some experience with the oref1 algorithm in the hopes of fine-tuning my diabetes management during sports and to try out unannounced meals.
I was conflicted in the beginning as I was seeing results consistent with my goals on FreeAPS (Loop), but It was something I wanted to try. I am glad that I did.
The first few weeks on AAPS can be painful as you learn the system and go through the objectives. I had completed as many as I could using a virtual pump so It wasn’t long before I was able to close the loop again this time using AAPS. I expect my numbers will improve once again when automations are available to me.
Setup and Configuration:
For the first few days I was still using the Dexcom IOS app as my collector app sending my BG readings to the King King Mini 2 (KKM2) to be processed by AAPS, this worked well as I was always in areas with reception. I did this because I wanted to be able to switch back to Free-APS quickly if I decided that AAPS wasn’t working for me. The first sensor on the KKM2 I paired with xDrip+. I loved xDrip as it provided heaps of additional data, but I had issues with delayed and missed readings. I have now switched to the the Dexcom BYOD app and this seems to be proving readings more consistently. I am using an Anubis transmitter but I am unable to validate battery level with the Anubis Tool on the KKM2.
Stats:
Last 22 days on Loop (FreeAPS)
…
First 21 days on AAPS
Likes:
Improved Time-in-range (TIR) (+7.7 %), average blood glucose (-0.2 mmol/l), GVI, PGS and A1C even though AAPS was new to me and I was still figuring a few things out. I achieved this exercising less than I usually do due to weather.
Better control with less work.
More flexibility – The ability to scale and tailor your meal or correction dose is awesome (include trend, IOB, COB, correction percentage and your blood glucose readings in the the calculation for increased control and precision).
Fewer (0.2% less) low events.
Quicker to respond to bring high blood sugars down.
Unannounced meal management (UAM) using the Oref1 algorithm (not tested).
No Apple Developer licence fee (I paid less for my KKM2 than I was paying for my annual developer license).
Easier to setup and deploy to the KKM2 than the Free-APS (Loop) app.
Remote (SMS) bolus.
The ability to super bolus (include basal for a specific period with a bolus).
Super micro bolus’ (SMB) are more effective at dealing with gluconeogenesis from high protein meals.
Autosens has been useful by identifying periods of insulin resistance or sensitivity and adjusting basal accordingly.
Dislikes:
My pump (Medtronic 522) is using batteries more frequently (60% quicker on AAPS).
Bluetooth (BT) drop-outs more frequently than loop. In the last 21 days I have had 6 ‘Pump unreachable’ errors and 3 ‘Missed BG readings’. This resulted in elevated blood glucose during the evening.
The KKM2 battery drains faster when I am around multiple other Bluetooth enabled devices than what the iPhone did.
The connection between the Phone (King Kong Mini 2) to the Orangelink and pump seems a little less stable than with the with the iPhone and loop, but I suppose you can expect a far inferior Bluetooth chip on a phone that costs a 10th of the price. This is easily remedied by restarting the Orangelink , turning BT on and off or in some cases, usually with the the pump unreachable error, I had to restart my phone.
I really liked the ICE (Insulin Counteraction effect) data in Loop. It was useful to see where I went wrong with my previous bolus and AAPS doesn’t have this data readily available like Loop did. If you were using UAM it would be unnecessary.
Is the new Coles low-carb bread type 1 diabetic friendly? Yes I think it is, read more below to discover why I think it is.
Review
Nutritional Information
Insulin Strategy
Goal
Results
When trying anything new I always read the nutritional information on order to determine the impact it will have on my body. Certain high fat foods can cause insulin resistance and inflammation and will delay gastric emptying while protein will digest and get synthesised into carbohydrates.
Insulin Strategy
The strategy I used for this meal was based on the insulin type and macro composition. I injected for the carbs + (1/2 fibre) right before I ate as the Fiasp I use works almost immediately and so no pre-bolus was required. I then extended carbs at (protein*25%) + (fat *10%)
Goal:
The goal of any insulin strategy would be to inject enough insulin at the correct time so that the upward force the carbohydrates exert is counteracted by the downward force the insulin exerts and you stay in range for the duration of the meal.
To analyse this I use three (3) values, standard deviation, time in range (TIR) and Coefficient of the variation. These three (3) values will assist you in determining how good or bad a meal was for you in terms of blood sugar impact (BGI).
Time in Range (TIR): For TIR we are looking for a high percentage of your readings within a normal (I use 3.9-7.8 mmol/l ) range.
Standard Deviation: For standard deviation I look for values under 1 as a meal that has little to no blood glucose impact (BGI).
Coefficient of the variation (CV): Is the standard deviation divided by the average glucose. Its a measure that helps normalise the results by reducing the influence on average glucose. Most studies indicate that anything under 33% is good.
Results:
As we can see in the table below, the low-carb bread paired with the correct insulin strategy resulted in very stable blood sugars over a number of hours.
Time in Range (TIR): 100% (average)
Standard Deviation (SD): .5 (average)
Coefficient of the Variation (CV): 10% (average)
Read my post on some common foods I eat to gain a better understanding of how this meal impacted me in comparison.
It’s been 73 days since I started looping. I have had a difficult December with a sprained wrist from a mountain bike accident and myself, my daughter and my wife had gastro which resulted in very little sleep and some abnormal readings. In fact I am still having abnormal sensitivity to insulin, resulting in frequent lows or blood sugar swings. I also had two failed Dexcom sensors and moved to the code calibration method which resulted in two days of false high CGM readings in comparison to my blood glucose readings. I’ll add the CGM stats once I am finished the analysis. Hello 2022!
Blood Glucose Stats
Blood glucose stats
A marginal improvement in December over the fist two months, but I still have a lot of work to do to get to my goal of a 5.5% A1C. Interestingly enough, after the gastro I am now 40% more sensitive to insulin, so hopefully now that I am aware of this I can get back to better blood glucose readings. I will also need to run in open-loop when changing Dexcom sensors to avoid all the issues I was previously having with false high blood glucose readings causing my Loop to micro-bolus incorrectly or increase my basal in error. Still not sure how I will handle protein in open-loop.
Exercise Stats 2021-2015
Total distance exercised
Total time spend exercising
I more than doubled (55%) the amount of hours I spent exercising in 2021, mostly as I was working from home which allowed me to spend more time exercising. As my A1C lowered, my fitness levels improved dramatically.
Improved time-in-range during exercise with decreased exercise anxiety
Lets start with A1C and Time-in-Range (TIR)
If we look at my stats just prior to looping, I had an A1C of 5.6% and a time-in-range (TIR) of 78.5%. The GVI and PGS stats were also really decent (more on these metrics here https://bionicwookiee.com/2020/02/26/cgm-metrics-gvi-pgs/), with a GVI of 1.2 (non-diabetic) and a PGS of 29 (non-diabetic). A decent average of 6.4 mmol/l, and 3.4 % (1h4min) of time in the 3.0 mmol/l – 3.9 mmol/l range.
Now we look at my last month while on Loop. In order to reflect the learning curve involved from switching to a pump, I broke the stats into two (2) fortnightly blocks.
First two (2) weeks on LoopLast two (2) weeks on Loop
As can be seen in the charts above, some slight improvements are seen in all metrics discussed above with a 7.6% reduction in TIR and a 4.5% (-0.3) reduction in average blood glucose. The GVI and PGS metrics reflect modest variability and good control, as opposed to the previous non-diabetic results. I spent 22min (2%) in the 3-3.9 mmol/l range, down 10min from the previous periods 32min.
Reduce diabetic burden
This goal is subjective and difficult to quantify. Loop does make it easy to administer insulin, enable an over-ride, track carbohydrate absorption ( I was doing this with Spike) correct a hypo/hyper and even just wake up in-range. It does come with its own challenges and hurdles to overcome, like ensuring you have an up-to-date version, checking certificate expiry, ensuring your CGM is calibrated accurately, and then the challenges of constant site changes, reservoir and battery changes, insulin mixing and exercise.
Decrease food anxiety while increasing food options
It definitely feels like less of a burden to experiment with food or eat more carbs, as Loop can pick up any slack due to incorrect bolus calculations, or adjustments after exercise. I used to have 3.4 % (1h4min) hypos (3.0 mmol/l – 3.9 mmol/l) in a month due to incorrect dosing after exercise, but this number has significantly reduced to 2% (22min) while using Loop, as basal can be dynamically adjusted to fluctuations in blood glucose. Post prandial (meal) hyperglycaemia has also significantly been reduced, but I think in part due to Fiasp as it starts working immediately once injected.
MDI Average Carbs per day: 92.6 (*excluding ‘fake carbs’)
Loop Average Carbs per day: 121 (*including ‘fake carbs’)
*’Fake carbs’ are entered into Loop to manage the blood sugar spikes from gluconeogenesis (fat/protein synthesis into glucose)
Improve time-in-range (TIR) during exercise with decreased exercise anxiety
Unfortunately since switching to Loop the Python code I wrote to analyse blood glucose broke with the switch to Loop, so I only have the pre-loop analysis. I was quite happy with the control I had during exercise while on MDI.
I have included the table I have been updating while I work on the Python code, which doesn’t seem to accurately reflect the amount of hypo events I have experienced while running. On the whole cardio has been the item on my list I have struggled with the most, and has been a significant source of anxiety. I am quite certain that after a few months I will have a strategy nailed down and the anxiety associated with exercise will wane. As can be seen in the below table, I am currently focusing on running as its the exercise I am struggling to gain control over the most. I was able to stay in range for the entire duration of all my weight sessions.
I’ll write a follow up post in the next month before I start my Android APS experiment. Good luck fellow Loopers!!
So, I accidently ripped out my cannula whilst asleep last night… It wasn’t a great night!
The alarms started sounding at around 11. of course I woke and used my phone to administer some insulin not questioning what was happening. I did this a few times before I checked my reading with a meter and realised I was 15mmol/l and had ketones of 1.9mmol/l. So I checked my site and saw it hanging. Oops.
I quickly administered some Fiasp and changed my site, but it was already too late and I ended up puking. Lesson learned.
Day 26 of this adventure… Its cannula and reservoir change day. I’m slowly getting the hang of it now.
Day 5 of the Dexcom experiment. So far I am not sold. I certainly don’t feel its more accurate than the Libre + miao miao combo (especially with spike as the collector). Once a Libre was calibrated correctly I felt pretty comfortable that I wasn’t having a hypo but the Dexcom was telling me I was a stable 5.6 mmol/l.
I am keen to analyse the accuracy between the two methods. Let the data collection begin.
Dick The Diabetic's APS Adventures 