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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

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My diabetes history

Control Statistics for the last 5 years

Date Started Test	Control Mechanism	e-A1C	Average Blood Glucose	Time In Range (TIR) 3.9 – 10	Standard Deviation	Average carbs consumed	Coefficient of the variation	GVI	PGS	CGP – PGR
20/11/2019	MDI	6.1%	7 mmol/l	87%	2.2 mmol/l		31%	1.2	20.33	1.7
20/11/2020	MDI	5.6%	6.3 mmol/l	94%	1.7 mmol/l	< 60	27%	1.17	8.67	1.3
20/11/2021	Loop	5.7%	6.5 mmol/l	94%	1.7 mmol/l	<100 (carb counting)	26%	1.25	8.29	1.3
04/02/2022	Android APS	5.7%	6.5 mmol/l	96%	1.5 mmol/l	>200, little to no carb counting	23%	1.24	5.70	1.2
01/08/2022	Android APS – UAM	5.7%	6.5 mmol/l	95%	1.6 mmol/l	No carb counting with pre-bolus	25%	1.32	7	1.3
Last 3 months	Android APS – UAM	5.6%	6.4 mmol\l	95%	1.6 mmol/l	No carb counting with pre-bolus	25%	1.34	7.5	1.3

Analysis stats provided by Nightscout reporter.

Exercise statistics for the last 5 years

Year	Average Time in Range (3.9-7.8 mmol/l)	Average blood glucose (mmol/l)	Average Standard Deviation (mmol/l)	Average Coefficient of the variation (%)	Total Hours	Total KM
2023	77.4%	6.7	0.56	12	143	1027
2022	75.6%	7.04	0.43	16	131	885
2021	71.9 %	6.7	0.4	17	149	920
2020	69.7 %	6.9	0.7	13.6	67	658
2019	66%	7.27	0.58	12.6	16	146
2018					37	333

Annual improvements are made through tweaking system variables and my approach to exercise.

A1C conversion chart with explanation

A1C level conversion chart help convert A1C in % to BS in mg/dl & mmol/L using DCCT formula.

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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 🙂

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.