I started testing unannounced meals in Android APS on Saturday 30 July 2022. I am a bit of a control freak and really couldn’t believe that a system could manage my diabetes better than I could. I had more information available to me to make more informative decisions “I would tell myself”. But I seem to be wrong, well at least partly. I am using a branch of AAPS that delivers insulin early, but I found that managing protein and fat was more problematic than carbs seemed to be. Stubborn high blood sugar that seemed to take a few hours to correct. So I decided to do a little testing with automatons to try and improve those numbers.
Boost UAM stats (Time in range 3.9-7.8 mmol/l)
Boost UAM stats (Time in range 3.9-10 mmol/l)
Announcing carbs and pre-bolusing
Eerm…what? How is this possible? It seems with accurate carb counting I still cant account for digestion times as well as AAPS can.
Automations
I use two (2) automations to try and manage my readings more closely. These automations are over and above the Boost logic that provides insulin earlier than the standard code.
The first automation simply sets a lower target when my reading is above 7.8 mmol/l AND not dropping. This allows AAPS to bring down my readings more quickly.
The second is to try and compensate for protein and fat in the low carb meals I eat. This automation will activate if
my reading is above 6.5 mmol/l AND
between meal times AND
my reading isn’t dropping AND
there is active resistance detected (not sure if this even matters)
My hypothesis is that the system can detect the resistance post the meal window but I need to test this assertion further.
I decided to purchase the Omnipod Dash trial pack of 10 pods for $30 AUD to see what all the hype was about. It turns out the hype is warranted, as this is an incredible little system. I’m very excited to use the device under a multitude of conditions and I hope that my experience can be informative. My main testing criteria will be connectivity, recovery in the unlikely event a Pod is damaged, robustness during various activity, water resistance and general day-to-day activity including time with my two year old daughter.
Benefits
The pump system operates much the same as any other pump system available, with the main difference being that the pump and cannula are all part of the same physical unit. This is a huge advantage for sports, but can be noticeable while changing clothes, going to the toilet or during sexy time. The unit is so small its presence is barely noticeable.
Omnipod Dash.
Sugar Management stats (So far)
I am very pleased (and surprised to be honest) that I am using 28% less insulin on the Pods with improved (+9.3%) blood sugar control (Time in Range 3.9-7.8 mmol/l). I noticed far fewer super micro boluses (SMBs) being administered than before, but maybe that is due to me letting AAPS do more of the work in managing my sugars through unannounced meals (UAM).
Management Stats from Nightscout for the duration of the experiment so far. TIR = 3.9-7.8 mmol/l
Total daily dose (TDD) and carbs average for the duration of the experiment.
Note: I am not adding in all the carbs I am eating as I am using announced meals in AAPS.
The Ambulatory Glucose Profile (AGP) enables retrospective analysis of dense data, trends and patterns for the duration of the experiment. Management Stats from Nightscout for the week prior to the experiment so far. TIR = 3.9-7.8 mmol/l
Total daily dose (TDD) and carbs average for the week prior to the experiment.
The Ambulatory Glucose Profile (AGP) enables retrospective analysis of dense data, trends and patterns for the week prior to the experiment. Management Stats from Nightscout for the duration of the experiment so far. TIR = 3.9-10 mmol/l
Screenshot from AAPS highlighting the SMB’s.
Android APS Setup
Setup of the Pod system in Android APS (AAPS) Boost Master 3.6.4 was surprisingly easy and intuitive. I just followed the Prompts after going to the configuration builder and selecting Dash as the pump.Its a very similar process for Eros pods, with the added requirement to pair the OrangeLink / RileyLink device.
I had an error starting the pod, but after hitting retry multiple times the pod activated and all was working as expected
Exercise
Exercise has been a lot more enjoyable without all the wires and having to worry about pump placement or damage. If I mountain bike and fall off (which happens every now and again) I lose one pod, and not an entire pump. Having more pocket space and less to carry is an added benefit.
Whats next?
I plan to test the pod while resistance training, mountain biking, running and the most intense sport I play, wrangling my two year old. If she cant destroy them, they are indestructible 🙂
An interesting set of results from my time with Boost. I cant wait to return to AIMI with what I have learned.
I was eating slightly less carbs (118g vs. 136g) than when I was using AIMI, but I would expect that if your profile is setup correctly that the amount of carbs shouldn’t have much of an impact on performance metrics. I was running into a few hypos so I lowered the algorithms insulin required percentage (70% vs. 100%) which resulted in a whopping 1.6% decrease (57 minutes vs. (288 minutes / 4.8 hours)) of time spent in a hypoglycaemic state while using Boost.
My standard deviation was slightly lower (1.3 vs. 1.4) signifying less fluctuations between readings while my time in a hyperglycaemic state went up 0.6% (25.2 hours vs. 26.6 hours)).
I did have to turn off “enable boost percentage scale” as this was providing too much insulin, but more experimenting could remedy this.
Another interesting observation was that my ratio between total daily dose (TDD) and total carbs eaten went down from 4.2 to 4.01 (grams of carbs per unit of insulin) as my average blood sugar lowered.
My exercise seems to have stayed pretty consistent during testing (as per the table below) which leads me to believe the reduced insulin need may be related to requiring less insulin when blood sugar is euglycemic. Its going to be a little more difficult to track that for the next few weeks as I am preparing to ramp up training for the Southern Cross 10km in July. The training has however provided an opportunity to fine tune my running routine as outlined here in my post about predicting blood sugars while running.
I cant wait to do some further testing with AIMI, Boost and Eating now.
Table of performance metrics from when I was MDI to the latest build of Boost.
Boost Stats (70% insulin required)
AIMI Stats (100% insulin required)
Boost TIR stats 3.9 – 10mmol/l
Exercise table (stats derived from Strava and Python scripts)
I recently posted about how my experimenting with running using AAPS was progressing. I observed that under certain conditions there was a significant lag between the capillary blood and CGM readings at about the 30 minute mark. This sparked some interest in wanting to know if I could predict what my capillary blood sugar could be using machine learning. I’m still not sure if its entirely possible yet, but I am having fun trying, and I have learned a lot in the process. I’m currently working on the script that will allow me to overlay a lot more AAPS data during a workout, including predictions.
I am getting a lot better at managing blood sugars as I noticed the last 14 exercises I was in range (3.9-7.8) 100% of the time.
Python script data capture
I have changed my approach slightly towards eating before exercise and have piggy backed off some research done by Gary Scheiner to create a spreadsheet that estimates the carbs required and effort for a run.
Dynamic ISF is all the buzz in the diabetic community at the moment, and so it should be! In the first few days of using it I was able to see marked improvements with little to no intervention as the algorithm scaled my correction doses.
What is Dynamic ISF
Your insulin sensitivity factor (ISF) is how much one unit of insulin will lower your blood glucose. Simply put dynamic ISF uses your total daily dose (TDD) of insulin to scale this value depending on blood sugar.
My results
So far the results have been astoundingly positive.
Before D-ISF : Average TDD: 30.1, Average Carbs :138gUsing D-ISF: Average TDD: 25.1, Average Carbs :102g
Update:
Using D-ISF for one (1) month. A little bit of an unfair test as I had 3 sensors fail during this period, which resulted in many issues with sensor accuracy.
Formula
ISF = 277700 / ( BG * TDD )
Differences in the GUI (when using DEV)
As you can see in the picture below, the yellow square highlights the time the loop last ran, and the red square shows the profile ISF vs. the calculated dynamic ISF.
Home screen of AAPS
It’s interesting to note that the D-ISF code isn’t implemented when using the calculator, its only actively scaling ISF using SMB’s (and maybe basal %). This will mean your normal profile ISF is going to be used when eating a meal. If you have never tested your ISF It may be worth while checking what the scaled ISF is at meal time (provided your BG is perfect) and setting your profile to that value if the values are vastly different. This is also providing that you aren’t in a period of significant resistance or sensitivity.
The Dynamic ISF plugin you will obtain in the config builder of the Dev code.
Where do I get a copy of the AAPS Dynamic ISF branch
It is in the same repository as the standard (master) version of AAPS. In order to use it you will need to select a different branch of AAPS (dev) and build that branch. I suggest you read Tim Streets repo notes for a more in depth description of the code and its functionality, even though you will not require the code from that repository. You will need to enable engineering mode on the phone in order to utilise the dev branch.
I decided to try using an automation to lower insulin levels and raise my glucose target before doing cardio. This allows AAPS to start this process at 05:30am on my days of choice so that my body is ready to exercise safely and with less need to consume carbohydrates. In my limited testing the process is working well, with some slight tweaking for testing parameters needed. NOTE: I only added half the amount of carbs I consumed to the APS for tracking. This is to avoid overcorrecting by the algorithm.
Expectations
I am trying to find the ideal conditions to exercise where I can experience moderate blood glucose fluctuations and not be required to consume large amounts of carbohydrates to keep me exercising safely. In the past on MDI I used to exercise fasted with only basal on board, which allowed me to stay in range for about 40 minutes before needing carbs. I am hoping to achieve this same amount using a pump. In past experiments I was able to achieve similar results during exercise by significantly reducing basal rates but I found that post exercise I struggled with higher than usual blood glucose readings for a few hours due to lack of insulin in my body.
Automations
Blood Glucose vs. CGM
The CGM results differed during exercise an average of 25% from blood readings. This made me decide to start some research of my own into using machine learning to try and estimate my blood glucose during exercise.
Results / observations
The automation route works well if you plan your exercise far enough ahead. The next experiment I will drop the profile percentage to 60% and observe. I noted an average of about 25% difference between the results the CGM and the finger pick tests. I was however able to keep my readings in range 100% of the time using 34g of carbs for the duration of the 50 minute experiment.
Capture from Nightscout
Video
I created a video using data from my Garmin Forerunner 245 and AAPS to track the experiment. In this video I track blood glucose, insulin, carbs, basal, distance, heart rate and cadence. I noted that the algorithm the Garmin uses to determine distance does not work well while walking and didn’t register any distance until I started lightly jogging.
In preparation for my cycle I started an automation to prepare my body for the impending exercise. This automation reduces my basal insulin ( as well as scale the rest of my management metrics) by 30% and set a temporary target (TT) of 7mmol/l. AAPS will not allow me to automate a profile % shift of more than 30%, so I reduced the profile a further 5% manually in AAPS an hour before the ride.
Exercise Metrics:
Garmin exercise stats
Blood Glucose:
Interestingly, AAPS stopped basal for a long period and allowed the IOB to runs its course.
My blood glucose held quite steady despite a mixture of anaerobic and aerobic levels of activity and so I didn’t need to consume any carbohydrates. Hopefully future attempts are as successful as this one.
Dick The Diabetic's APS Adventures 