Analysing 2022 exercise data from AAPS

Disclaimer: The information contained within this blog post are my thoughts and do not constitute medical advice. Please consult your medical team before making any changes to your diet or blood sugar management program.

So far 2022 has been quite the year. With the return to my work offices Its been rather difficult to reach many of the goals I set myself, but I did make progress. It seems 2023 is set to be a particular difficult year, but perhaps this will be the inspiration I need to make some positive changes. The Python scripts I wrote to export data from Nightscout to create my mountain bike videos seem to be working well and I can’t wait to make a few more videos.

I was curious to see if there were any differences in insulin sensitivity between longer and shorter activity durations, as well as higher intensity (where average heart rate was more than 80% of max heart rate) training and it seemed there was, it just wasn’t what I was expecting.

My average total daily dose (TDD) for 2022 was 32.9 units per day. If we analyse my aerobic activity (ride and runs) for the year and we use my sensitivity ratio from AAPS for 24 hours post exercise, I calculate that I saved 256 units of insulin in 2022 through exercise due to increased insulin sensitivity. During aerobic activity I consume 12g of carbs on average per 30 mins of activity unless I am exercising fasted. I can use this input to calculate that I ate 2277g of carbs during 2022. I would need 311 units of insulin to absorb 2277g of carbs. Since I don’t add carbs to AAPS while exercising I don’t have the exact numbers but I do believe this calculation to be pretty accurate. That equates to 49 Big Mac burgers / 82 Apples / 73 slices of Dominos peperoni pizza that I got to eat without insulin as a direct result of exercise.

Exercise metrics

Analysing my exercise metrics I found that I was spending way too much time exercising at more than 75% of heart rate max, this would be hampering performance and building endurance. I did eighteen (18) runs at a distance greater than 8km, an improvement over the two (2) I did in 2021. I also managed my longest run ever at 16km.

exercise typeexercise counttotal distance (km)average distance (km)average moving time (minutes)average heart rate (bpm)
EBikeRide720.642.9518.65N/A
EBikeRide ( > 8 km)17252.415.759.4133.8 (72% max HR)
Run108374.253.4723.5139.26 (75% max HR)
Run ( > 8 km)18183.110.167156 (85% max HR)
Walk4865.81.3718.693 (50% max HR)
WeightTraining650.0033.77105 (57% max HR)
TOTAL2628965.636125 (68% max HR)
Exercise stats table for 2022

Time-in-range (TIR)

The longer distance running seem to result in the best time-in-range (TIR) (3.9-7.8 mmol/l) but I do feel that these runs also seem to happen at a similar time in the morning where I have more control over insulin-on-board (IOB) and carbs-on-board (COB) and I am the most resistant to insulin. My heart rate is also far more consistent (aerobic) during running than when mountain biking ( aerobic / anaerobic ).

If I start digging into the data for short runs more closely I find that;

  • TIR (3.9-7.8 mmol/l) from 04:00am – 10:00am is 63%
  • TIR (3.9-7.8 mmol/l) from 10:00am – 13:00pm is 83%
  • TIR (3.9-7.8 mmol/l) after 13:00pm is only 23%
exercise typeexercise counttime-in-range (%)
EBikeRide781.67
EBikeRide ( > 8 km)1665.56
Run10856.8
Run (04:00 – 10:00 am)1863.8
Run (10:00 – 13:00 pm)6183.6
Run (13:00 – 10:00pm)2923.02
Run ( > 8 km)1893.6
Walk4575.8
WeightTraining6587.7
Exercise time-in-range table for 2022

Blood glucose control metrics

The exercise that resulted in the lowest blood glucose fluctuations is walking with a CV of 4%. The exercise with the second lowest CS was weight training. I generally try to train with a little insulin-on-board to counteract the hormones released during training and I don’t need to set a high temp target in the lead-up to the activity, thus my reading is much lower at exercise commencement. The third lowest is short runs (< 8km) with CV of 6%. The higher blood glucose average will be a direct result of me setting a higher temp target (8 mmol/l) prior to exercising, but the duration of activity isn’t long enough to reduce the blood glucose substantially resulting in the high average. Long runs seem to result in the least stable blood glucose values with a CV of 12% but the average for long runs is lower as the sustained activity reduces blood glucose. I suppose on these longer runs I do consume a minimum of 30g of ultra-fast acting carbs (glucose, dextrose) which is going to result in some fluctuations in blood glucose.

With coefficient of the variation (CV) a lower percentage is indicative of more stable blood glucose readings.

exercise typeexercise countaverage standard deviationaverage blood glucoseaverage coefficient of the variation (CV)
EBikeRide70.577.498%
EBikeRide ( > 8 km)160.9210.69%
Run1080.437.196%
Run ( > 8 km)180.696.2411%
Walk480.276.774%
WeightTraining650.46.396%
Exercise breakdown for 2022

Insulin sensitivity

A very interesting observation was that longer, more intense activity resulted in sensitivity returning to normal quicker than less intense or shorter activity. Runs shorter than 8km resulted in a massive 12% insulin reduction for 24 hours post activity, that’s around 6.5 units less insulin in a 24 hour period. Long E-Bike rides resulted in the largest increase (35%) in sensitivity 1 hour post activity, with shorter E-Bike rides the second largest increase in sensitivity. Runs longer than 8 km increased sensitivity (25%) the third most, but the body seemed to return to normal more quickly than the shorter runs and was almost back to normal within 12 hours of activity.

(NOTE: I can’t comment on the validity of the results, only that patterns exist after exercise that are not usually observed in the absence of aforementioned exercise.)

average insulin sensitivity
exercise typeexercise count1 hr post exercise3 hr post exercise6 hr post exercise8 hr post exercise12 hr post exercise24 hr post exercise
EBikeRide71091051031029995
EBikeRide ( > 8 km)16687888939779
Run1088692959610298
Run ( > 8 km)18768092949794
Walk48105109111112114109
WeightTraining6595101100106110104
Average insulin sensitivity for multiple time blocks post exercise grouped by exercise type.

Profile Adjustments vs. Temporary Targets (TT)

In the past I used a combination of a 30% reduction in profile and a temporary target of 7 mmol/l while exercising.

This seemed to work quite well, with the caveat that profile adjustments can result in your autosens data being reset if you cancel the adjustment earlier than set.

One way to combat this is to set a higher temp target, this will not effect sensitivity data and can be cancelled at any time without needing to update the basal insulin profile in the pump of effecting autosense data. In order to do this I analysed the adjustments I was using to calculate a temp target that should reduce my insulin enough to keep me in range for the duration of activity.

TargetTemp_TargetInsulin % reducedActual % of profile30% Reduction20% ReductionNote
5.3851%49%This resulted in quite a few low blood sugars
5.38.560%40%2023 backup temp target strategy
5.38.357%43%2023 temp target strategy.
5.37.542%58%28.5%38.5%
5.57.027%73%42.7%52.7%Strategy in early in 2022

Thank you for reading 🙂

Diabetes awareness month and Android APS

14/11/2022

Introduction

It’s diabetes awareness month and so I wanted to talk to you about something I am very passionate about, Android artificial pancreas system (AAPS). It’s not the cure I wanted but as far as I am concerned, it’s the closest to normal I have felt in the 25 years I have been a diabetic.

Why I LOVE Android APS

I decided to try Android APS just prior to the birth of my daughter. At the time I was using multiple daily injections (MDI) on a low-carb diet (less than 45g per day excluding protein and fat) and trying to pick up some muscle. I found it rather challenging to eat the number of carbs the trainer suggested without compromising control. I was also anticipating the late nights having a baby entails and I wanted to be prepared. David Burren’s blog provided a blueprint of what could be expected if I committed to investing the time required to perform all necessary testing and fine-tuning.

Benefits of Android APS

  • Meal management
    • Meals can be managed through a number of mechanisms including;
      • Un-announced meals (UAM) – AAPS boluses insulin without intervention or carb entry.
      • Announcing carbs – Add the carbs into the system and the calculator estimates the amount of insulin required based on your COB, IOB, ISF, current blood glucose, blood glucose deltas, and insulin sensitivity.
      • Extended carbs – typically used to mimic the absorption of protein (gluconeogenesis) or delayed gastric emptying caused by high-fat meals.
  • Exercise management
    • Insulin scaling adjusts basal insulin based on current insulin sensitivity
    • Automations allow you to schedule profile changes and temporary blood sugar targets for the duration of activity or condition.
  • Hardware / pumps / wearables
    • Functional on many old and new low-cost Android phones
    • Directly or indirectly (via Nightscout) display various blood glucose-related data on compatible watches. If you are using an Android watch (WearOS) you can control AAPS via the watch. Garmin watches can display blood glucose data during an activity.
    • Control a wide variety of pumps
    • Utilise the blood glucose data from a wide variety of CGMs (continuous glucose monitors)
  • Software
    • Automations allow you to automate system actions based on conditions (eg. blood glucose increasing, blood glucose decreasing, leaving for work, pump disconnect) or schedules.
    • SMS Commands to control AAPS remotely
    • Super micro boluses / boli (SMBs) allow AAPS to provide insulin efficiently and effectively.
    • The system will suspend insulin delivery when blood sugar is predicted to go below a certain threshold.
    • Highly customizable to your unique needs, with certain advanced builds allowing you to control more system variables (Boost, AIMI, Eating Now).
    • Cutting-edge development
      • Dynamic insulin sensitivity factor (ISF that changes based on blood glucose)
      • Improved prediction models
      • Improved insulin modeling (9-hour DIA)
  • Quality of Life
    • Reduced diabetic burden and stress.
    • Glucose is constantly monitored, with the ability for someone to follow you remotely, including community members. This can assist with fine-tuning settings.
    • Ability to eat more foods without compromising control
    • Improved glucose control reduces the possibility of long-term complications.
  • Safety
    • Objectives provide a level of safety as users need to understand basic principles of how to use the APS prior to closing the loop.
  • Cost
    • AAPS is open-source and free to use.
  • Monitoring / Reporting

Dis-benefits of Android APS

  • DIY Software build
    • As with all DIY systems, you are required to build the application prior to using it.
  • Cost of hardware
    • Phone
    • CGM
    • Pump and supplies
  • Connectivity fatigue
    • The burden of being connected to technology 24/7
  • Reliance
    • It is easy to become reliant on AAPS managing blood sugars.
  • Usability
    • Due to its complexity, you are required to invest a large amount of time in order to gain the understanding and skills required to configure and utilise it correctly.

Statistics and examples:

Nightscout statistics – 3 Months

Nightscout blood glucose distribution report
Nightscout blood glucose weekly distribution report

Control stats for different systems

Date Started TestControl Mechanisme-A1CAverage Blood GlucoseTime In Range (TIR) 3.9 – 10Standard DeviationAverage carbs consumedGVIPGSCGP – PGR
20/11/2019MDI6.1%7 mmol/l87%2.2 mmol/l1.220.331.7
20/11/2020MDI5.6%6.3 mmol/l94%1.7 mmol/l< 601.178.671.3
20/11/2021Loop5.7%6.5 mmol/l94%1.7 mmol/l<100 (carb counting)1.258.291.3
04/02/2022Android APS5.7%6.5 mmol/l96%1.5 mmol/l>200, little to no carb counting1.245.701.2
Analysis stats provided by Nightscout reporter.
Comprehensive glucose pentagon from Nightscout reporter report.

Un-announced meal (UAM) example

Low-carb meal with UAM running (Low-carb bread with cheese, ham, and mayo.)

Extract from Android APS data for a low carb meal

As can be seen above the system manages low-carb meals quite well with no carb inputs from the user. The system constantly monitors for rapid changes in blood sugars and administers insulin when required to quickly brings sugars into range.

Nightscout screenshot of low carb meal being absorbed while AAPS manages sugars.

Exercise stats / examples

YearAverage Time in Range (3.9-7.8 mmol/l)Average blood glucose (mmol/l)Average Standard Deviation (mmol/l)Total HoursTotal KM
202280.1 %6.60.43 131885
202171.9 %6.7 0.4149920
202069.7 %6.9 0.767658
Annual improvements are made through tweaking system variables and my approach to exercise.
Weight Training
DateIOB @ startMoving timeExercise TypeAverage HR (bpm)Standard Deviation (mmol/l)CGM BG StartCGM BG EndCGM BG Average (mmol/l)TIR (3.9-10)
2022-10-070.1436.93WeightTraining101.10.3657.76.87.27100.0%
EBike Ride
Android APS data exported during an E-Bike Ride 2022-11-06.
DateIOB @ startMoving timeExercise TypeDistance (km)Average HR (bpm)Standard Deviation (mmol\l)CGM BG StartCGM BG EndCGM BG Average (mmol\)TIR (3.9-10)
2022-11-06-0.849115.92eBikeRide271431.0955.95.87.16100%
Running
Android APS data exported during a run 2022-10-03.
DateIOB @ startMoving timeExercise TypeDistance (km)Average HR (bpm)Standard Deviation (mmol\l)CGM BG StartCGM BG EndCGM BG Average (mmol\)TIR (3.9-10)
2022-10-03-0.53478Run12 1681.1055.55.16.75100%

Thirty day challenge – week 3

Summary

I am starting to feel like a routine is forming, perhaps not around diet yet, but definitely in regards to training. In previous years of doing this I was eating clean most days, as it provided improved diabetic control in the absence of an APS/AID and pump.

This week was particularly heavy due to my birthday dinner, a new phone, a new version of AAPS (Boost test platform 3.6.5) and a 25 km cycle. *I have been unable to pair my galaxy watch with my new phone, which is sad as I really liked the watch and having the plethora of sensors.

I was investigating the possibility of measuring insulin sensitivity changes in AAPS . One way would be to use the autosens feature in AAPS , but since I wasn’t including the carbs I ate to fix hypos, and I was snacking in-between to keep my readings steady that wasn’t going to work. The only metric that may prove useful may be my carb sensitivity factor (CSF). The average CSF over the 22 days so far is 8.7 while the average sensitivity ratio was 106%. This would mean that according to CSF I was 36% more sensitive to carbs yesterday or 29% less sensitive according to autosens.

Body Metrics

StartWeek 1Week 2Week 3Week 4
Weight (kilograms)75.8747574.1
Body fat percentage (according to Samsung)17.3%17.8*
Body fat percentage (according to the navy seal calculator)15%15%14.8%
Total volume
Table stating the weekly body metrics I am tracking.

Exercise

Week 1Week 2Week 3Week 4
Distance (kilometres)25.1720.5437.22
Activity (hours)4.343.655.64
Table stating the weekly exercise metrics I am tracking

Nutrition

Screenshot of average macro-nutrients consumed during week 3
Screenshot of average macro-nutrients consumed during week 3

Diabetes

Week 1Week 2Week 3Week 4
Low (<3.9) (%)0.90.63.5
In Range (3.9-7.8) (%)75.374.771.9
High (>= 7.8) (%)23.824.724.6
Standard deviation (SD) 1.31.71.7
Average (mmol/l)6.87.0 6.7
A1c estimation (%)5.96.05.8
Table stating the weekly diabetic metrics I am tracking.

Ideally I want to see a time-in-range (TIR – 3.9-7.8 mmol/l) exceeding 90% with an average in the low sixes and a standard deviation (SD) around one (1).

Analysis of an exercise challenge (with graphs)

Scenario

Before my run yesterday my blood glucose was lower than I felt was safe to start exercising (around 4 mmol/l ) despite reducing insulin to 70% of requirements and setting a higher temp target ( 7 mmol/l) an hour prior to exercising. I decided to eat an Anzac cookie (20g carbs) and accidently devoured some deep fried bread (30g of carbs). This was excessive to say the least ( I needed around 12g according to my calculator). It also created a complicated situation where I would need to inject prior to exercise or forego exercising all together and rather focus on injecting the appropriate amount of insulin (8.62 units) to counteract the carbs.

Factors to consider

As a rule of thumb I try to have as little insulin on-board as possible as this reduces the chances of hypoglycaemia (low blood sugar). Due to the carbs I ate I would now need to inject insulin prior to exercising or my blood glucose would rise excessively. I factored in that both fat (from the fried bread) and exercise would decrease the speed of digestion. Exercise decreases the speed of digestion as energy used for digestion would be diverted to my heart, lungs and muscles to fuel my activity and fat slows down gastric emptying and causes insulin resistance.

Insulin calculation

Utilising my IC ratio and exercise table I calculated I required around 1.7 units of insulin prior to the commencement of my run. I decided to be cautious and inject 1.5 units. I used a total of 4.4 units to return to euglycemia (normal blood sugar).

Insulin-to-carb ratiocarbsInsulin Required75%50%25%20%15%
5.8508.626.474.312.161.721.29
Insulin calculation table
Table containing the amount of carbs I require for a specific duration of activity. This was derived from information found on the internet and my own experience.

Results

The results were by no means perfect, but at the very least I learned how much insulin I require and more or less when I should be injecting after I finish exercising. Its extremely difficult to predict how much exercise will amplify insulins activity, so it’s always safer to take a cautious approach.

My final decision was to inject 1.5 units prior to starting my activity and inject the remaining amount once my sugars started rising due to digestion resuming (around 35 minutes after stopping exercise).

Blood glucose vs. row count

My blood sugar peaked at 10.5 mmol/l after only 1.5 units of insulin for 50g of carbs.

I used a total of 4.4 units of insulin (50% of the total insulin required) to return to euglycemia after my activity. My average blood glucose was 6.6 mmol/l and my time-in-range (TIR) was 100% for the duration of my activity.

Graph with glucose control metrics during activity
Distance (km)Moving time (minutes)Average heart rateStandard deviationCGM BG startCGM BG endCGM BG minCGM BG maxCGM BG averageTime in rangeBG twenty minutes post exerciseBG sixty minutes post exercise
Lunch Run4.92 29.53170.20.3086.946.176.176.946.66100.05.899.0
Glucose control metrics
Daily overview graph with insulin and carbs.
Diabetic management metrics.

My blood sugar control after exercise improved and I was happy with the results for the remainder of the day. Next time I would inject 20 minutes post exercise to prevent digestion resuming causing hyperglycaemia (high blood glucose).

Thirty day challenge – Week 1

Summary

The first week was challenging to say the least. As I have increased my physical activity my insulin needs seem to have fundamentally changed, and this resulted in quite a few lows. I also had some tech issues, my Android phone had an operating system error and my Dexcom sensor wasn’t enjoying the resistance training I was doing as it was inserted in my arm. I reached my activity goals but exceeded my diabetic and nutritional goals.

Body Metrics

StartWeek 1Week 2Week 3Week 4
Weight (kilograms)75.874
Body fat percentage (according to Samsung)17.3%
Body fat percentage (according to the navy seal calculator)15%
Total volume
Table stating the weekly body metrics I am tracking.

Exercise

Week 1Week 2Week 3Week 4
Distance (kilometres)25.17
Activity (hours)6.24
Table stating the weekly exercise metrics I am tracking
Week 1 breakdown

Nutrition

Screenshot of the average and total macronutrients consumed during week 1.
Screenshot of average macronutrient consumed during week 1

Diabetes

Ideally I want to see a time-in-range (TIR – 3.9-7.8 mmol/l) exceeding 90% with an average in the low sixes and a standard deviation (SD) around one (1).

Glucose Analysis Tool

Two years ago my daughter was born. At this time I was off work for a few weeks and I had been strongly considering writing a tool that could provide some insight into managing my blood sugars, as I knew controlling my blood sugar was the best chance I had of being the best parent I cold be. At the time I was on multiple daily injections (MDI), leveraging heavily on Dr Bernstein’s teachings and using daily (if possible) exercise as a tools for glucose control.

Two years later the tool has pivoted many times, and at one time I was using machine learning (ML) to predict blood glucose during exercise, until I started using the Dexcom G6 continues glucose monitor (CGM) which was accurate enough to circumvented the need for the aforementioned tool.

At present I am using an automated insulin delivery (AID) device to deliver most of my insulin based on my shifting needs. This significantly reduces the mental burden required for good glycaemic control, as well as reduce some of the anxiety I was experiencing at meals times and when going to the doctor for diabetic checks. This system still requires manual input prior to exercise and constant tuning if you want to have the best experience, and so my tool has pivoted towards analysing this data and providing insight there.

Diabetic Metrics

An analysis of the last 4 years of my diabetic journey highlights a better A1C with a lower standard deviation (SD) indicating more consistency in blood sugars. Its interesting to note the much improved time-in-range (TIR-IN) metrics once I moved over to a insulin pump using an automated insulin delivery (AID) device.

PeriodHypo (below 3.9)In (3.9-10)High (above 10)AverageA1CSDGVIPGSPGRPGR-RiskExercise hoursKilometresPump / MDI
201919%77%3%7.76.50%2.61.2640.192.2Low16146MDI
20208%88%3%6.96.00%2.11.1917.341.6very-low67658MDI
20213%94%3%6.45.60%1.71.188.791.3very-low149920Pump (Loop) 20/11/2021
2022 (YTD)3%96%1%6.55.70%1.51.25.431.2very-low75496Pump (AAPS)
Table displaying the last few years worth of diabetic data.

For a description of some of these values mean please read this article.

Goals

My goal was to provide some insight into what was working and was not. To do this I needed to obtain blood sugar readings as well as nutritional and exercise data. I achieved this by creating a tool that obtains data from Nightscout, Strava and MyFitnessPal This data is then processed and enriched to provide insight. I then developed a tool to export some of this data and display it on my YouTube videos. I had it connected to Garmin to extract sleep and exercise data but the Garmin API failed and I have not had time to update the program.

My tool will then do some analysis to provide some insight at a per meal or per activity level by looking at metrics like time-in-range, average glucose, standard deviation, max glucose, min glucose and many more metrics.

Below are some example’s of some of the data I am exporting and using to make decisions.

This tool is very much still under constant development, as I am always finding new stats to display and bugs with some of my current code (at present both the Garmin API and the MyFitnessPal API have issues)

Below are some graphs and tables that I created in my Tool (The graphs are generated in DB Browser, these will at some stage be created in a JS library or Python graphing library).

Analysis of BG vs ISF, vs Sensitivity after a run.
Analysis of BG vs ISF, vs Sensitivity after gym.
Average TIR (time-in-range) and average blood glucose per exercise type for 2022.

Return-to-Range

I use this table to understand how quickly the system is able to reduce sugars into a normal blood sugar range. At the moment I am using 8 and 6.

Return-to-range analysis (by year)

In 2019 It took just over 6 hours to return to euglycemia (blood glucose < 6) after a peak, in 2023 I managed to reduce that to 3.2 hours.

The average time it takes to return to a blood glucose of 6 mmol/l after a peak.
The average time it takes to return to a blood glucose of 8 mmol/l after a peak.

Daily Sensitivity Analysis

I find this useful to determine if I am more sensitive to insulin on certain days, usually due to exercise.

Exercise Sensitivity Analysis

The exercise sensitivity data has been updated to be hourly for 12 hours post exercise. Its now calculated via SQL (insert statement) and not a Python function into a staging table.

APS Version Analysis

APS Version Battery Analysis

Exercise Stats Analysis (per exercise)

Exercise Stats Analysis (annual)

In the hopes of improving time-in-range while exercising I experimented with reducing insulin and used these values to provide insight into wether the changes were successful or not. In 2019 I was in-range only 66.6 of the time, in 2023 I am in-range 75%, with a slight improvement in glucose while exercising.

Treatments Analysis (per treatment)

Treatment Type Analysis

I use this table to understand how frequently I am interacting with the loop. This has little impact on the version of the variant of Android APS I am using.

Strava diabetic stats

I wanted to see diabetic statistics for each event in Strava, so I wrote a script to update the description field with some data I calculating in the Python tool. The script will check to see if the description field is populated and only update records that have no data in the description field.

Data to follow:

https://1drv.ms/u/s!AmpIRdFk1_yMgosDqE73Osiyl30ZAg?e=w9sIzl

Improvements

There are a number of improvements I am working on.

  • Web Interface

Thirty day challenge

It’s spring, and after a brief ‘almost two months’ of going off the reservation snacking at all times of day and barely exercising, I decided to check my weight. I discovered I had picked up a few kilograms since my last weigh in. After learning this, I decided that it was time for me to get my life back together and start another 30 day challenge. I find these great to provide the reason to get back into a routine.

I know that setting unrealistic goals (like losing 5kgs) isn’t going to work, so I’m going to break down my plan in to nutrition, exercise and diabetes goals.

Exercise

My plan for the month is to gym three days a week, run a minimum of 2 times per week and to mountain bike at least once a week. (So I guess I lied about setting unrealistic goals 🙂 )

Nutrition

For my meals I plan to stick to my usual low-ish carbohydrate meals during the week and try to only go coo-coo bananas on the late night snacking over the weekend. I’ll start carb-counting again as this will almost always yield the best results. This will be supplemented with 2-3 liters of water, depending on length of cardio that day.

Diabetes

Above is a chart of my starting metrics. Lets see how quickly I can improve those values. Its going to be a little bit of an unfair test as I was not carb-counting during the above period.

We want to see the In range (Time-in-range) increase and the standard deviation decrease. By doing that the average and the A1c should follow. This will mostly be achieved by the diet component of the plan. The exercise component will allow me to eat more cabs and require less insulin, as well as improve circulation, sleep, blood pressure, mood, cholesterol, memory and overall mental and physical health.

I will check in with weekly updates to ensure I keep motivated and accountable.

Video overlay description

I have been working on a repeatable process to create YouTube videos that contain my AndroidAPS (AAPS) device data stored in Nightscout. My first attempt used a Python script to export a CSV file. This needed to be started by saving a note in Nightscout, which I often forgot to do just before I started riding. My most recent update stores the devicestatus (https://your-nightscout-site/api/v1/devicestatus) API call into a database and does not require any external trigger to start the logging process. This data is updated every five (5) minutes as a calculation cycle completes in AAPS. The below is an explanation of some of the fields I am exporting.

Description of values present in the video:

  • IOB (Insulin-on-board) – The amount of active rapid-acting insulin you have in your body.
  • COB (Carbs-on-board) – The estimated number of grams of carbohydrates in your system that are waiting to be absorbed into your bloodstream
  • Basal – The primary job of basal insulin is to keep your blood glucose levels stable during periods of fasting, such as while you’re sleeping
  • Uploader battery – The battery % of the uploader device.
  • BGI (blood sugar impact) – The algorithm uses BGI (blood glucose impact) to determine when carbs are absorbed.
  • Insulin Required – The amount of insulin the algorithm calculates you require to return to a euglycemic state.
  • Finger prick BG – The blood sugar reading from a standard finger prick test.
  • Blood sugar – The CGM blood sugar value.
  • CSF (carb-sensitivity-factor) – The carb rise ratio (by some also called CSF, carb sensitivity factor) describes by how many mg/dl our glucose rises per gram of absorbed carbohydrate.
  • Dynamic ISF (insulin-sensitivity-factor) – An insulin sensitivity factor (ISF) or correction factor describes how much one unit of rapid or regular insulin will lower blood glucose. Dynamic ISF is calculated based on your total daily dose of insulin.

Omnipod Dash – Summary – Week 1&2

Its only been a week and already I feel so comforted by the barely audible click of the pump depressing the plunger in the mini pump at meal times or sporadically throughout the day. Its the sound of blood sugar control. What a week its been learning all I can about Pod changes and being woken up on day 3 by the Pod alarm alerting me its 8 hours before the Pod expires. Once expired it was interesting to note that the Pod functioned as per normal, apparently for another 8 hours.

I had a Pod on days 3 and 4 that was inserted into my leg that may have had a cannula issue, as I struggled to maintain my standard level of control.

Its been a lot easier to exercise focusing on enjoying the task rather than if I would break the pump or rip out a cannula. Having no wires makes it a lot easier to run or gym as I don’t have to worry about pump placement as much. Previously I needed to ensure I had pants with pockets or a belt clip available.

I have also found sleeping a little easier, as I can barely notice the pump If I roll over onto it.

Flank insertion.
Boost Omnipod – Time in Range (3.9 -7.8 mmol/l)
Boost Omnipod – Time in Range (3.9 – 10 mmol/l)

Unannounced meals

I decided to test the system with unannounced meals consisting of 40g of carbs or less. I am a bit of a control freak when it comes to diabetes so I have been postponing testing this for a long time. The results were outstanding. I will be writing more about this in the future, including any automations I use or test.

Boost Omnipod – UAM – Time in Range (3.9 -7.8 mmol/l)

Boost Omnipod – UAM – Time in Range (3.9 – 10 mmol/l)

Android APS – AIMI, Boost & EN

The efficacy of any AID (Automated Insulin Delivery) device is directly proportional to the amount of insulin it provides in relation to your needs, and everybody’s needs are slightly different. To ensure safety the native AAPS loop only administers 50% of the calculated insulin requirement per loop cycle. This limitation can be overcome by using one of the branches I mention below, or by profile switching, using temp targets and automations.

AIMI

Pros:

  • A ton of unique features seen nowhere else (dynamic SMBRatio, DIA change for every SMB or Bolus, if TIR above 180 during the last two hours and up of 30 % the profile during the next hour if it’s still the case,if the TIR below 75 one hour before and reduce the profile during the next hour,calculation to propose one basal value for the day,A new function is working, after the manual bolus > iTime_starting_bolus, AIMI will force the basal 500% during the next 20 minutes.)
  • Frequent updates
  • A lot of collaboration with the diabetic community.

Cons

  • The DEV branch takes much longer to calculate cob using my KK mini 2. This can result in periods where I think insulin may be delivered later than preferred.
  • The battery life of your loop device is significantly impacted due to multiple calculations.
  • Watch seems to be less stable with more periods of stale data
  • More frequent pump disconnects.

Thoughts: I don’t think I gave this a fair go. I should have adjusted to use less insulin. I think this is more suitable to people using UAM.

AIMI
AIMI

BOOST

Pros:

  • Best control I have ever had
  • Adjustable and scalable insulin required percent (SMBRatio). example. You can set Boost to provide 75% of the required insulin %, but if the glucose deltas are greater that percentage can increase to a maximum of 150%.
  • The setup process is straight forward.

Cons:

  • Less frequent updates

Thoughts: Suitable for people who enter carbs or who use UAM. Works well with Dash pods.

BOOST
Boost

Eating Now

Pro:

  • Frequent updates
  • A lot of unique features seen nowhere else (too many to mention here, read the docs)
  • Excellent documentation
  • The reasons (device status) behind why the system is adjusted are well documented and clear in the loop status and the documentation.
  • Lots of safety functions in the code.

Con:

  • You can adjust insulin required percentage but its (as far as I am aware) a static value.

Thoughts: Works extremely well when entering carbs and pre-bolusing.