Category: Food

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Half Marathon weekly updates

Week no.DistanceElapsed timeAvg HRTIR (3.9 – 7.8) (%)Average Blood Glucose (mmol/l)Coefficient of variation (%)Strava Fitness Metric
Week 1032257132697.012.037
Week 936.29242.65133.95875.017.042 (+5)
Week 834.1240.94152.979.876.03.145 (+3)
Week 767.5295.01136.4696.06.29.054 (+9)
Week 646.36340.12133.073.567.316.253 (-1)
Week 540.57224.91142.689.436.412.255 (+2)
Week 429.46208.03140.290.06.2954 (-1)
Week 38.7573.37142.9100.06.08.749 (-5)
Week 223.14146.82153.6350.06.818.648 (-1)
Week 125.4169148608.42251 (+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

AIMI – ModelAI

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

AIMI – ModelAI

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

AIMI – ModelAI

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

AIMI – ModelAI

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

AAPS – Boost 3.9

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

AAPS – Boost 3.9

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:

  1. 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.
  2. 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.
  3. 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

My diabetes history

Control Statistics for the last 5 years

Date Started TestControl Mechanisme-A1CAverage Blood GlucoseTime In Range (TIR) 3.9 – 10Standard DeviationAverage carbs consumedCoefficient of the variationGVIPGSCGP – PGR
20/11/2019MDI6.1%7 mmol/l87%2.2 mmol/l31%1.220.331.7
20/11/2020MDI5.6%6.3 mmol/l94%1.7 mmol/l< 6027%1.178.671.3
20/11/2021Loop5.7%6.5 mmol/l94%1.7 mmol/l<100 (carb counting)26%1.258.291.3
04/02/2022Android APS5.7%6.5 mmol/l96%1.5 mmol/l>200, little to no carb counting23%1.245.701.2
01/08/2022Android APS – UAM5.7%6.5 mmol/l95%1.6 mmol/lNo carb counting with pre-bolus25%1.3271.3
Last 3 monthsAndroid APS – UAM5.6%6.4 mmol\l95%1.6 mmol/lNo carb counting with pre-bolus25%1.347.51.3
Analysis stats provided by Nightscout reporter.

Exercise statistics for the last 5 years

YearAverage 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 HoursTotal KM
202377.4%6.70.56121431027
202275.6%7.040.43 16131885
202171.9 %6.7 0.417 149920
202069.7 %6.9 0.713.6 67658
201966%7.270.5812.6 16146
201837333
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.

Featured

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%
Featured

Thirty day challenge – week 4

Summary

This week was the best so far. I did my longest run to date (12km) and had a really good gym week lifting (approx. 20% more volume). My diabetes control is improving (thank you AAPS and exercise) and I have learned a lot digging though my data and through responses from the previous weeks question regarding carb sensitivity factor (CSF) being used to measure insulin sensitivity post exercise. I made some strides in my glucose management tool which also felt great.

BG vs. ISF vs. insulin sensitivity post gym (@11:48:09 – 42min)
BG vs. ISF vs. insulin sensitivity post run (@11:49:30 – 33min)

After gym sensitivity increased to 115% directly post training, while my sensitivity was stable at 78% post my run.

Body Metrics

Body mass vs. body fat
StartWeek 1Week 2Week 3Week 4
Weight (kilograms)75.8747574.173.1
Body fat percentage (according to Samsung)17.3%17.8**
Body fat percentage (according to the navy seal calculator)15%15%14.8%14%
Total volume
Table stating the weekly body metrics I am tracking.

Exercise

Week 1Week 2Week 3Week 4
Distance (kilometres)25.1720.5437.2229.4
Activity (hours)4.343.655.645.4
Table stating the weekly exercise metrics I am tracking

Nutrition

Screenshot of average macro-nutrients consumed during week 4
Screenshot of average macro-nutrients consumed during week 4

Diabetes

Week 1Week 2Week 3Week 4
Low (<3.9) (%)0.90.63.51.6
In Range (3.9-7.8) (%)75.374.771.978.9
High (>= 7.8) (%)23.824.724.619.5
Standard deviation (SD) 1.31.71.71.5
Average (mmol/l)6.87.0 6.7 6.5
A1c estimation (%)5.96.05.85.7
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).

Featured

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

StartWeek 1Week 2Week 3Week 4
Weight (kilograms)75.87475
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 1Week 2Week 3Week 4
Distance (kilometres)25.1720.54
Activity (hours)4.343.65
Table stating the weekly exercise metrics I am tracking

Nutrition

Screenshot of average macronutrient consumed during week 2
Screenshot of average macronutrient consumed during week 2

Diabetes

Week 1Week 2Week 3Week 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)1.31.7
Average 6.87.0
A1c estimation5.9%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).

Featured

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).

Featured

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.

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)

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.