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Half Marathon weekly updates

Week no.	Distance	Elapsed time	Avg HR	TIR (3.9 – 7.8) (%)	Average Blood Glucose (mmol/l)	Coefficient of variation (%)	Strava Fitness Metric
Week 10	32	257	132	69	7.0	12.0	37
Week 9	36.29	242.65	133.95	87	5.0	17.0	42 (+5)
Week 8	34.1	240.94	152.9	79.87	6.0	3.1	45 (+3)
Week 7	67.5	295.01	136.46	96.0	6.2	9.0	54 (+9)
Week 6	46.36	340.12	133.0	73.56	7.3	16.2	53 (-1)
Week 5	40.57	224.91	142.6	89.43	6.4	12.2	55 (+2)
Week 4	29.46	208.03	140.2	90.0	6.2	9	54 (-1)
Week 3	8.75	73.37	142.9	100.0	6.0	8.7	49 (-5)
Week 2	23.14	146.82	153.63	50.0	6.8	18.6	48 (-1)
Week 1	25.4	169	148	60	8.4	22	51 (+3)

Week 10 (24th – 30th)

Week 10 is done and dusted. During my long run on Friday I decided to use the New Balance Fresh Foam More V3 running shoes to see if that improved my experience. This is because they are the most padded shoes I own, and the small 3 mm drop is supposed to reduce the risk of injury. Unfortunately this only reduced the pins and needles, but I must have laced them poorly as I got a few blisters. I also started my run way to quickly, and if I cant maintain a few zone 2 runs I am not going to work my slow twitch muscle fibers and improve my fitness fast enough to enjoy this race. These were shoes I had used before so I think it was down to the lacing and running technique. The last run of the week was a 8 km zone 2 run on the treadmill. This was actually the first of run week 9, but I decided to mountain bike ride on the Monday as it was raining on the Sunday so I switched them. For this run I used the New Balance 1080v12 shoes, and this was a great run. Not sure if it was due to treadmill suspension but every second felt good, despise a hypo half way though.

Exercise stats

Blood glucose stats

My experience with AIMI AI has not been a good one, for some reason I keep going on the blood sugar roller coaster and the system either gives too much or too little insulin. I am eating way less at the moment so I would expect more control. I’m back to Boost once this Pod expires to try and improve my glucose values. Probably not a great time to be trying a new system while increasing my training.

Measurements

Weight 75.6 kg

Week 9 (01st – 07th)

In an attempt to reduce the pins and needles I was experiencing I got some 2XU vector compression socks. This seemed like it may have improved the experience somewhat, until I started wearing my Brooks Ghost shoes. During the 8km run with the Ghosts I experienced no issues whatsoever. Blood sugars with AIMI seemed to be much better this week, except for a few isolated incidences where AIMI provided too much insulin. Since AI (or in this instance machine learning) requires data to build its model accurately, its seems likely that I needed more data in order for the system to perform better.

Exercise stats

Blood glucose stats

Measurements

Week 8 (08th – 14th)

This week was was a big training week with the start of the extended distance (15-20 km) runs in the training plan. I have bee using the Ghosts more and although I no pain during my runs, I had quite a few blisters afterward the 15 km run. No vector socks this week. The long run was a little difficult to manage with my sugars requiring (22g+25g+28g) 75 g of carbs to stabilize for the run. I wasn’t expecting that. Quite a few more hypers that lasted longer than I had hoped. I think this is my last week of testing AIMI-AI before heading back to good old Boost. I like the fact I can very accurate with IOB with Boost, although my difficulty is due to AIMI adapting so well with all the changes. I also found myself on my Strava groups leader board twice 🙂

Its been interesting to see the difference between how Garmin and Strava track fitness, with Garmin using Vo2 max as its measurement and providing a stamina metric.

Exercise stats

Fitness

Below is a post highlighting the difference between the two systems in relation to fitness metrics.

Fitness metrics, Garmin vs. Strava

Strava fitness metrics

The Strava fitness metric seems to build with every run, providing some motivation to keep hitting those zone 2 runs. I am still 10 fitness points lower than I was in December. I am currently averaging 6 points every two weeks. If keep at this pace I should reclaim my fitness in about 23 days, or by the 6th of June. So that leaves almost the whole of June to work on improving fitness.

Garmin fitness metrics

The Garmin fitness metric is Vo2 max, or maximal oxygen consumption. This refers to the maximum amount of oxygen that an individual can utilize during intense or maximal exercise. This measurement is generally considered the best indicator of cardiovascular fitness and aerobic endurance.

Blood glucose stats

Measurements

Week 7

This week I decided to shake things up, do a little mountain biking in New South Wales and also travel to new beach locations for my runs. It was amazing and I had an incredible time. I have also been asked to be a front runner for the Gold Coast Marathon team training sessions at HOTA.

Exercise stats

Blood glucose stats

Measurements

Week 6

This weeks long run was a little harder than usual, but it did have a beautiful view. I wanted to do a 15km but unfortunately was cut short to 14km. My feet felt good and good issues with blistering with the Ghosts. I also did a park run which almost ruined everything as my tendon issue faired up with the lack of a proper warm up. I was a little slower than I thought as I only managed 5min/km for 2,5km before burning out.

Exercise stats

Blood glucose stats

Measurements

74kgs

Week 5

This week’s long run was fantastic! I completed the full 16 km without experiencing any pain or discomfort. As an experiment, I decided to try a Cliff Bar for the first time during my run, and I think I may have found my new go-to snack for long-distance running. I started my run with a blood glucose level of 4.1 mmol/l and waited approximately 20 minutes after consuming the bar before getting started. This slight delay caused a small spike in my glucose levels at the beginning of the run. To mitigate this, I plan to wait only 15 minutes before starting my next run.

Analyzing the graph below, we can observe when the Automated Insulin Delivery Systems (AAPS) kicked in to provide a temporary basal rate adjustment to lower my blood sugar levels. Since I set a slightly higher temporary target of 8.3 mmol/l, AAPS registered my insulin sensitivity to be around 55% less than my standard needs. AAPS responded perfectly, gradually bringing my glucose levels down to a comfortable 5.2 mmol/l by the end of the run.

Overall, this run was a success, and the combination of the Cliff Bar and the effective response of AAPS made it even better. I’m excited to continue fine-tuning my routine and exploring the benefits of different strategies to optimize my long-distance running experiences.

Exercise stats

Blood glucose stats

Measurements

74 kgs

Week 4

This week was a bit of a mixed bag for me as far as my diabetes management goes. On the one hand, I had a great park run, where I managed to run a respectable 5:10 min/km for the 5km duration. This landed me in second position overall for this particular park run.

My long run started off very strong, but towards the end I developed some pain in the glute which resulted in me needing to stop the run at 17km, rather than the planned 18km. I managed to stay in range 100% (3.9-7.8 mmol/l) for the duration of the run with an average of 5.8 mmol/l, and my standard deviation was 1.024. I attribute to this to the cliff bar I ate 15min prior to starting the run.

I had a high percentage of low blood glucose readings on my CGM this week, which was mainly due to CGM sensor issues. This, in conjunction with poor rest has resulted in my HRV being quite for low for the week.

I went one full work week without sugar free soda. This change was due to recent research released indicating the significant detriment to health sugar free soda can have.

Exercise stats

Blood glucose stats

Measurements

Week 3 (12-18th)

Exercise stats

Blood glucose stats

Measurements

Week 2

During my last long run, I had to make the difficult decision to bail out early. Unfortunately, I was dealing with an ankle and tendon injury, which limited my capacity to cover the desired distance. Additionally, my blood sugar levels dropped significantly, adding another layer of challenge. Upon reflection, I realized that this low blood sugar episode was a consequence of inadequate planning. I had not set a high enough temporary target of 8.3 for a sufficient duration, leading to the drop in blood sugar levels.

To alleviate some stiffness in my legs, I sought a massage on Sunday. However, this revealed another issue – lower back pain on the left side. It became evident that this discomfort was likely a consequence of my existing tendon problem on the left side, as my body attempted to compensate for the imbalance. While I have been diligent in incorporating stretching exercises into my routine, it is unfortunate that I began doing so too late to make a significant impact on my current situation.

Despite the challenges I have faced, I consider these setbacks as valuable learning opportunities. Moving forward, I intend to implement the following lessons to prevent similar situations:

Prioritize Injury Prevention: Understanding the importance of injury prevention, I will be more cautious with my training and listen to my body’s signals. This means recognizing the need for adequate rest, seeking professional advice when necessary, and gradually increasing intensity and distance.
Establish Effective Blood Sugar Management: To avoid experiencing low blood sugar levels during physical activities, I will proactively set higher temporary targets and ensure their duration aligns with the demands of my workouts. This way, I can maintain stable energy levels and perform optimally.
Address Imbalances and Compensatory Patterns: By acknowledging the connection between my tendon issue and the resulting lower back pain, I will incorporate exercises and therapies that specifically target these areas. By addressing imbalances early on, I can prevent further complications and improve overall performance.

While my fitness has undeniably declined due to the limitations imposed by my injuries, I have gained valuable insights from these experiences. By emphasizing injury prevention, refining blood sugar management, and addressing compensatory patterns, I am confident in my ability to overcome these setbacks and continue progressing on my fitness journey. Remember, setbacks are not roadblocks but opportunities for growth and resilience.

Exercise stats

Blood glucose stats

Measurements

Week 1

The lead up to race week has been less than ideal. Unfortunately my injury is preventing me from training and is causing pain and discomfort when I run, especially at incline. This was a reminder that injury prevention is key, and if I ever attempt this again I will ensure I follow a program that prioritises injury prevention through intelligent training, gradually increasing mileage and strength training. Please read my retailed post about the race below.

Gold Coast Half Marathon

Exercise stats

Blood glucose stats

Measurements

Featured

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 type	exercise count	total distance (km)	average distance (km)	average moving time (minutes)	average heart rate (bpm)
EBikeRide	7	20.64	2.95	18.65	N/A
EBikeRide ( > 8 km)	17	252.4	15.7	59.4	133.8 (72% max HR)
Run	108	374.25	3.47	23.5	139.26 (75% max HR)
Run ( > 8 km)	18	183.1	10.1	67	156 (85% max HR)
Walk	48	65.8	1.37	18.6	93 (50% max HR)
WeightTraining	65	0.0	0	33.77	105 (57% max HR)
TOTAL	262	896	5.6	36	125 (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 type	exercise count	time-in-range (%)
EBikeRide	7	81.67
EBikeRide ( > 8 km)	16	65.56
Run	108	56.8
Run (04:00 – 10:00 am)	18	63.8
Run (10:00 – 13:00 pm)	61	83.6
Run (13:00 – 10:00pm)	29	23.02
Run ( > 8 km)	18	93.6
Walk	45	75.8
WeightTraining	65	87.7

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 type	exercise count	average standard deviation	average blood glucose	average coefficient of the variation (CV)
EBikeRide	7	0.57	7.49	8%
EBikeRide ( > 8 km)	16	0.92	10.6	9%
Run	108	0.43	7.19	6%
Run ( > 8 km)	18	0.69	6.24	11%
Walk	48	0.27	6.77	4%
WeightTraining	65	0.4	6.39	6%

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 type	exercise count	1 hr post exercise	3 hr post exercise	6 hr post exercise	8 hr post exercise	12 hr post exercise	24 hr post exercise
EBikeRide	7	109	105	103	102	99	95
EBikeRide ( > 8 km)	16	68	78	88	93	97	79
Run	108	86	92	95	96	102	98
Run ( > 8 km)	18	76	80	92	94	97	94
Walk	48	105	109	111	112	114	109
WeightTraining	65	95	101	100	106	110	104

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.

Target	Temp_Target	Insulin % reduced	Actual % of profile	30% Reduction	20% Reduction	Note
5.3	8	51%	49%			This resulted in quite a few low blood sugars
5.3	8.5	60%	40%			2023 backup temp target strategy
5.3	8.3	57%	43%			2023 temp target strategy.
5.3	7.5	42%	58%	28.5%	38.5%
5.5	7.0	27%	73%	42.7%	52.7%	Strategy in early in 2022

Thank you for reading 🙂

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Thirty day challenge – week 2

Summary

The second week I have gained a little weight (surprise its not muscle) and had a reduction in exercise hours, which was mostly due to a very long ride I had the previous week.

My diabetic metrics have declined and I feel like all of this mostly due to my diet which needs tweaking.

Body Metrics

	Start	Week 1	Week 2	Week 3	Week 4
Weight (kilograms)	75.8	74	75
Body fat percentage (according to Samsung)		17.3%	17.8
Body fat percentage (according to the navy seal calculator)		15%	15%
Total volume

Table stating the weekly body metrics I am tracking.

Exercise

	Week 1	Week 2	Week 3	Week 4
Distance (kilometres)	25.17	20.54
Activity (hours)	4.34	3.65

Table stating the weekly exercise metrics I am tracking

Nutrition

Screenshot of average macronutrient consumed during week 2

Diabetes

	Week 1	Week 2	Week 3	Week 4
Low (<3.9)	0.9%	0.6%
In Range (3.9-7.8)	75.3%	74.7%
High (>= 7.8)	23.8%	24.7%
Standard deviation (SD)	1.3	1.7
Average	6.8	7.0
A1c estimation	5.9%	6.0%

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).

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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 ratio	carbs	Insulin Required	75%	50%	25%	20%	15%
5.8	50	8.62	6.47	4.31	2.16	1.72	1.29

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).

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.

	Distance (km)	Moving time (minutes)	Average heart rate	Standard deviation	CGM BG start	CGM BG end	CGM BG min	CGM BG max	CGM BG average	Time in range	BG twenty minutes post exercise	BG sixty minutes post exercise
Lunch Run	4.92	29.53	170.2	0.308	6.94	6.17	6.17	6.94	6.66	100.0	5.89	9.0

Glucose control metrics

Daily overview graph with insulin and carbs.

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).

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.

Period	Hypo (below 3.9)	In (3.9-10)	High (above 10)	Average	A1C	SD	GVI	PGS	PGR	PGR-Risk	Exercise hours	Kilometres	Pump / MDI
2019	19%	77%	3%	7.7	6.50%	2.6	1.26	40.19	2.2	Low	16	146	MDI
2020	8%	88%	3%	6.9	6.00%	2.1	1.19	17.34	1.6	very-low	67	658	MDI
2021	3%	94%	3%	6.4	5.60%	1.7	1.18	8.79	1.3	very-low	149	920	Pump (Loop) 20/11/2021
2022 (YTD)	3%	96%	1%	6.5	5.70%	1.5	1.2	5.43	1.2	very-low	75	496	Pump (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

Omnipod Dash – Summary – Week 1&2

4th Aug 2022

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)

Unannounced meals – Week 1

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.

Quest Protein Bar T1D Review

Is the Quest protein bar 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.

Below are two great resources you should read before deciding on your final dosing strategy. Its important to note that in Australia, most items don’t have total carbohydrate count that includes fibre and sugar alcohol, which can make it difficult to assess the impact of products that don’t list sugar alcohols in the nutritional information.

Net Carbs Vs. Total Carbs: What Counts?

Counting Sugar Alcohols

Insulin Strategy

Based on the nutritional information above, my inulin to carb ratio and my proximity to recent exercise I decided to inject as follows; I didn’t input my eCarbs for the protein as I knew that AAPS would be able to manage. Read my post for injecting for protein and fat if you are not on an AAPS or experience elevated blood glucose two (2) hours after eating.

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.

Picture Source: See my CGM. https://seemycgm.com/2017/08/09/why-dia-matters/

Results

As we can see by the table below that this snack consumed with the correct insulin strategy resulted in very stable blood glucose over a number of hours, with little deviation. What should be noted is that the sugar alcohol started to effect readings after 3 hours and that 1 hour prior to consumption I had exercised. The exercise would have increased my insulin sensitivity.

Time in Range (TIR): 100%

Standard Deviation: 0.38

Coefficient of the variation (CV): 0.06

Read my post on some common foods I eat to gain a better understanding of how this meal impacted me in comparison.

Food Information table

8th Mar 2022

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.

Equipment/ Software: Dexcom G6, Android APS, Medtronic 522, OrangeLink Pro, Nightscout, Python, Excel

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).

Injecting for Protein and Fat with an Artificial Pancreas System (APS)

13th Feb 2022 (updated 18th October 2022 to include unannounced meals)

In recent years scientists have discovered that carbohydrates are not the only macronutrient that has an effect blood sugar. In fact there are more and more medical studies exploring the effects of the other macronutrients (fat and protein) on blood sugar, and the complications they pose to blood sugar control. In the post below I provide my understanding and explore how I inject for protein and fats using an artificial pancreas system (APS).

Science / Background

One of the more interesting reads on the subject is a medical study entitled “The Role of Dietary Protein and Fat in Glycaemic Control in Type 1 Diabetes: Implications for Intensive Diabetes Management”. In the study they explore the effect of fats and protein on blood sugar management. I have summarised some of the more interesting elements below;

Fat itself doesn’t get converted through gluconeogenesis into carbohydrates, but glycerol from the hydrolysis of TAGs can be metabolised to pyruvate and makes up between 5-15% of the weigh of TAGs, so only part of the TAGs would be converted into glucose.
Fat does have an impact on gastric emptying, slowing the process down by delaying and reducing glycaemic and insulinemic responses.
Meal protein influences hormones such as cortisol, growth hormone, IGF-1 and ghrelin. These changes impact insulin resistance and increase insulin requirements.
90% of the amino acids found in protein are converted into glucose through gluconeogenesis and will increase blood glucose.

How do I inject for the effects of fat and protein?

There are currently a few different methods to deal with the effects of protein and fat on blood glucose, I will outline the one I mostly use, but if you are on multiple daily injections (MDI) I strongly suggest reading Dr. Bernstein’s diabetes solution for an approach using regular insulin. All of these approaches require some experimenting on your part.

Loop / Android APS

Depending on the macronutrient profile, I shift my approach appropriately. I generally eat low-carb high protein meals as these meals are more easily managed by an APS or covered by MDI. For these types of meals I use the formula eCarbs (extended carbs) = (Protein *25%) + (Fat *10%). I then add an absorption time of around 5-6 hours, unless the fats are above 40g, then I would increase the absorption time to 7-8 hours to counter the delayed gastric emptying caused from the increased fat content.

eCarbs (extended carbs) = (Protein *25%) + (Fat *10%)

Example of a meal calculation

If the fat content is more than 60g, I prefer to add 30% to the total carb count and then split the dose 60/40 (this amount will vary on the protein content and your body’s ability to digest the food) . This can either be done by scheduling the second dose or by using an extended bolus.

Its often a hit and miss process. So I keep detailed notes to help me repeat the process should I guess correctly and want to eat that meal again. Otherwise its used as a variable and adjusted the next time I eat that meal.

Unannounced meals (UAM) – Android APS / OpenAPS

Another mechanism of managing high protein, moderate fat meals is though an Android APS / OpenAPS function called unannounced meals (UAM). This is extremely low input (All you need to do is not add carbs to AAPS). This requires you to manage your expectations somewhat in terms of how much fat AAPS can manage without user intervention, but moderate fat high protein meals can be managed with minor deviations and no user intervention as can be seen in the example below and is a complete game changer in terms of quality of life.

Goal

The goal of any meal as far as I am concerned is to keep your readings within a normal blood sugar range for the entire duration of the meal, whilst minimising any sudden spikes in blood sugar, essentially mimicking a working pancreas. To analyse this I use two values, standard deviation and time in range (TIR). These two (2) values will assist you in determining how good or bad a meal was for you in terms of blood sugar impact.

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).

Here are my top 5 meals in terms of standard deviation (SD) and time-in-range (TIR).

You will notice that all the meals are low-carb high protein meals. My blood sugar hardly deviated in the first 3 hours of eating and then had a slight bump during the absorption of proteins later. This can be seen by observing the differences in the three (3) and five (5) hour standard deviation values. The first set of standard deviations being lower represent blood sugars that are close to the each other in number and not changing much, then they start getting a litter larger as blood sugars rise (so the SD gets higher) as the proteins digest in the last 2 hours.

Example of Android APS (UAM) counteracting the absorption of proteins and fats

An export of the device status for the 4 hours post meal consumption.

In the graph above we can see sugars (grey) steadily rise and peak (8.2 mmol/l) at around the two (2) hour mark due to protein digestion (gluconeogenesis). By the four (4) hour mark the system has infused an additional 3.2 units of insulin through super micro boluses (SMBs) and temporary basal adjustments to bring sugars down to a comfortable, but not perfect 6.2 mmol/l. This was completely managed by AAPS with no intervention by myself. Using additional functionality in AAPS we could provide insulin more quickly using automations and profile switches to further reduce the impact of protein. If we use the Loop eCarb formula previously stated, then insulin required for fat and protein equates to 3.3 units.

Meal Name	Insulin	Carbs	Protein	Fat	Start BG BG	End BG	Average 2 hours	Average 4 hours	Time in Range 2 hours (3.9-7.8)	Time in Range 4 hours (3.9-7.8)
Lunch	2 units @ meal start	18.4g	62g	41g	5.8 mmol/l	6.2 mmol/l	6.69 mmol/l	6.87 mmol/l	85.1%	88.6%

Table providing results from UAM during lunch..

As you can see from the Nightscout graph, no carbs have been entered into AAPS and results have been outstanding.

Example of Loop counteracting the absorption of proteins and fats

In the picture below we can see how Loop manages gluconeogenesis (eCarbs or extended carbs) from a low-carb high protein meal I ate.

At the top of the Loop user interface (UI) (the yellow arrow) we can see that Loop is issuing a 2.3U temporary basal (temp basal) almost three hours after I injected for the meal. This temp basal is to counter the absorption of the fats and proteins. These temp basals’ are issued and adjusted multiple times during the course of the meal digestion timeframe.

In the Insulin delivery section of the Loop UI (the red arrow) we can see Loop delivering insulin in the form a temp basal. It takes multiple hours for the protein to be synthesised into glucose and so a temporary basal can easily manages the resultant rise in blood sugar from gluconeogenesis.

In the Active carbohydrates section of the Loop UI (the blue arrow) we can see that there are still 14g of unabsorbed carbohydrates that Loop will manage with temporary basals’.

The picture below is from the ICE (Insulin Carbohydrate Effect) chart displaying a graphical representation of the predicted vs observed blood glucose effects that insulin and carbohydrates have on the body.

A graphical view of the absorption process.

Meal examples

Here are a few examples of meals I eat, not all are low-carb meals, but all meals are under 50g of carbs.

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