Tag: Loop

Analysing exercise data for 2024

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.

I decided to get an analogue bicycle and I have loved the challenge of riding it. I had a terrible GC half marathon (GCHM), complete with muscle spasms, but I finished so that was nice. The training for the GCHM was amazing and I got to run in some pretty interesting places, like the NSW rail trail in Casino.

I have developed a host of new features for my Diabetes Analysis Tool, including an integration into Strava, where I update my exercise description with my exercise stats.

Physiological Metrics

I am currently on an average of 42.9 units per day and an average of 150g of carbs per day. These carbs include carbs from fat and protein (gluconeogenesis).

You can see from the graph below that my weight has fluctuated quite a bit this year, with poor eating habits (snacking at night) the biggest contributor to a lower time in range. My lowest bodyfat was 15% (confirmed by 3rd party testing). This dramatic weight shift was due to training for the GCHM.

Extract from the Renpho smart scale imported for Analysis.
Weight and Bodyfat graph exported from Diabetic Analysis Tool.

Exercise metrics

Every year I try to increase my distances and time in range (TIR). This year I increased my TIR by 2%, which is incredible. Although my CV and SD are lower, average glucose is down 0.06 mmol/l. I attribute this to lower insulin closer to exercise time, and refuelling at appropriate time intervals.

Annual view of exercise stats
2024 exercise stats (grouped by distance)
2023 exercise stats (grouped by distance)

Energy Burn Rates

A table of the estimated energy replacement carbs consumed.

Time-in-range (TIR)

A graph of Time in Range (3.8-7.8) per exercise.

Blood glucose control metrics

Extracted from Nightscout Reporter

Insulin sensitivity

In the below graph we can see that walking and weight training result in the lowest changes in sensitivity.

Graph derived from AVG_EXERCISE_STATS_2024_GROUPED_INSULIN_SENSITIVITY table.

Sleep Metrics

from GARMIN_MONTHLY_SLEEP_AVG
From GARMIN_MONTHLY_SLEEP_AVG
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My APS results vs. non-diabetic results

Recently my diabetic inspiration David Burren wrote an article about his results using an Artificial Pancreas System (APS). In this article he compared his results to those of 2019 CGM study of people without diabetes. His results are far better than my own, but I was interested to see how I stack up.

Further comparison with HCL and non-diabetic data

A table of my metrics vs. those a healthy individual using a CGM.

MetricNon-diabeticLast two (2) week*Last 3 monthsLast yearBoostAIMI AI
eHbA1c5.1%5.5%5.7%5.7%5.7%5.7%
GMI 5.7%5.5%5.8%5.7%5.7%5.7%
TIR (3.9-10 mmol/l)99%95%93%95%93%94%
TITR (3.9 – 7.8 mmol/l)97%86%79%79%77%80%
CV (%)16%24%28%26%28%26%
Average BG (mmol/l)5.56.26.56.56.56.5

*The last two (2) weeks of data with me being back in the gym.

GMI – Glucose Management Indicator

TIR – Time in Range (3.9-10 / 70-180)

TITR – Time in Tighter Range (3.9-7.8 mmol/l / 70 -140 mg/dL)

CV – Coefficient of variation

Analysis of current results

When analyzing my results on a glucose percentile diagram we can quickly see that the area I need the most work on is in the evenings. Making healthier choices here should have the most profound effect going forward.

Goals

I want to aim for an SD of less than 1.2 and an average BG of less than 6 to have a CV of 20% or less. This is considered to be an optimal range for non-diabetics. This equates to a TITR of around 90%.

Continuous Glucose Monitoring Profiles in Healthy Nondiabetic Participants: A Multicenter Prospective Study: https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7296129/

Glycemic Management Indicator (GMI)

The Glycemic Management Indicator (GMI) is an essential tool used to evaluate and monitor long-term glycemic control in individuals with diabetes. It is derived from continuous glucose monitoring (CGM) data or frequent fingerstick blood glucose measurements obtained over a specific period, typically ranging from two weeks to three months. GMI is considered an alternative to traditional HbA1c (glycated hemoglobin) measurements, offering a more comprehensive and insightful assessment of glucose management.

  1. Benefits of GMI Compared to HbA1c:

GMI provides several advantages over traditional HbA1c measurements:

a. Real-Time Data: Unlike HbA1c, which reflects an average of blood glucose levels over the past 2-3 months, GMI is based on current and recent glucose data. This real-time aspect allows for timely adjustments to diabetes management strategies.

b. More Comprehensive: GMI incorporates a broader range of glucose data, capturing variations and fluctuations in glucose levels that HbA1c may not reveal. It considers both hyperglycemia and hypoglycemia, offering a more comprehensive view of glycemic control.

c. Better Glucose Trend Assessment: GMI can monitor trends in glucose levels, helping healthcare professionals and individuals with diabetes identify patterns and make targeted interventions to improve glycemic management.

  1. Examples of Using GMI for Glycemic Control Monitoring:a. Personalized Treatment Plans: GMI data enables healthcare professionals to tailor treatment plans to individual needs. For instance, if a person has a high GMI with frequent hyperglycemic excursions, treatment adjustments can be made to reduce these excursions and improve overall glycemic control.b. Predicting Hypoglycemia: GMI can help identify periods of increased risk for hypoglycemia, allowing patients to take preventive measures, such as adjusting insulin doses or modifying meal plans.c. Assessing Treatment Changes: When implementing changes in diabetes management, GMI can track the impact of those changes over time, providing valuable feedback on the effectiveness of the modifications.
  2. Addressing Variations in GMI Calculation:The formula to calculate GMI is generally (Mean Glucose + 46.7) / 28.7. However, it’s essential to acknowledge that different devices, CGM systems, and methodologies might use alternative formulas or constants for GMI calculation. To obtain accurate and specific information on GMI calculation, users should refer to the guidelines and documentation provided by the manufacturer of the CGM device or the healthcare professional overseeing diabetes management. This ensures consistency and proper interpretation of GMI results.

In summary, GMI is a valuable tool in diabetes management, offering real-time insights and a more comprehensive assessment of glycemic control compared to traditional HbA1c measurements. By leveraging GMI data, healthcare professionals and individuals with diabetes can make informed decisions and take proactive steps to optimize diabetes management strategies.

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Gold Coast Half Marathon

Race Day

Introduction:

The Dawn Effect and Blood Glucose: When we wake up in the morning, our body experiences a surge of hormones, commonly referred to as the “dawn effect” or “dawn phenomenon.” This natural hormonal response can lead to an increase in blood glucose levels even before we consume any food or engage in physical activity. Cortisol, growth hormone, and other hormones play a role in this phenomenon. For individuals with diabetes, the dawn effect can pose challenges in maintaining stable blood glucose levels, especially during a race. The hormonal surge may contribute to higher blood sugar levels, making it crucial to adjust your diabetes management routine accordingly.

This graph shows the average blood sugar during training vs. my blood sugar from the Gold Coast Half Marathon.
Training vs. RaceAverage distance (km)Average time (min)Average HR (bpm)TIR (3.9 – 7.8)Average Blood Glucose (mmol/l)Coefficient of variation (%)Pace
Training 149215692%5.8146:40
Race21.414015121.4%9.922.546:39
This table shows the average metrics during training vs. the same metrics during the Gold Coast Half Marathon.

Blood Glucose Management: Pre-Race Strategies: To optimize your blood glucose levels during a race, careful planning and preparation are key.

Here are some strategies to consider:

  1. Race Day Automation: If you use an insulin pump or automated insulin delivery system, consider setting up a race day automation plan. Gradually reducing your insulin on board (IOB) and raising your blood sugar target before the race can help mitigate the impact of the dawn effect.

The automation I use if I plan on exercising at 06:30am. I use 05:00 – 06:00 so that if another automation is active at 05:00am there is opportunity for this automation to run after that one completes.
  1. Timing of Pre-Exercise Snacks: To align the digestion of carbohydrates with the energy demands of the race, it is important to time your pre-race snack appropriately. If your blood glucose is around 5 mmol/l before starting, consuming a carbohydrate-rich snack approximately 15 minutes before the race can help maintain stable blood glucose levels, in my experience cliff bars have the perfect amount of nutrients for a long run.
  1. Managing Blood Glucose During the Race: Once the race begins, various factors can influence your blood glucose levels.
  1. Here are some considerations to keep in mind:

    Listen to Your Body: Pay attention to any signs or symptoms that may indicate fluctuations in your blood glucose levels during the race. Feeling lightheaded, fatigued, or experiencing unusual thirst may indicate the need for carbohydrates. Regular Blood Glucose Monitoring: Carry a portable blood glucose meter to monitor your levels throughout the race. This will enable you to make timely adjustments and take appropriate remedial actions when necessary. Carbohydrate Consumption: Plan to consume carbohydrates during the race to maintain your blood glucose within a desirable range. Experiment with different forms of carbohydrates, such as gels, sports drinks, or energy bars, to find what works best for you. Remember to consider the impact of any exercise-induced insulin sensitivity and adjust your carbohydrate intake accordingly.

Data Extract from AAPS.

Post-Race Recovery: Upon crossing the finish line, it’s essential to prioritize your recovery and address any pain or discomfort that may have emerged during the race. Be mindful of the following:

  1. Musculoskeletal Discomfort: Races can place significant stress on your body. Pay attention to any pain or discomfort in your muscles, joints, or tendons. Consult with a healthcare professional if necessary to address any post-race injuries. Blood Glucose Check: After the race, continue monitoring your blood glucose levels as they may fluctuate due to post-exercise hormonal responses. Adjust your post-race nutrition and insulin dosages accordingly.

Conclusion: Participating in a race as a person with diabetes requires careful consideration of blood glucose management strategies. Understanding the impact of waking up on hormonal levels, adjusting your approach accordingly, and incorporating remedial actions during the race are crucial steps towards maintaining stable blood glucose levels. By staying vigilant, prepared, and responsive to your body’s needs, you can conquer the challenges of a race while managing your diabetes effectively.

References:

  • American Diabetes Association. (2021). Diabetes and Exercise. Retrieved from https://www.diabetes.org/healthy-living/fitness/exercise-and-type-1-diabetesGupta, L., Khandelwal, D., Singla, R., Gupta, P., Kalra, S., & Dutta, D. (2017). Dawn Phenomenon and Its Impact on Blood Glucose Control. Indian Journal of Endocrinology and Metabolism, 21(6), 901–909. doi: 10.4103/ijem.IJEM_284_17

Exercise stats from Garmin

Equipment

Equipment NameNote
Osprey duro 6 – Hydration packThis hydration pack is a great option for long runs or cycles. It holds 1.5 liters of water, which is more than enough for most people to drink on a 2+ hour activity. It also has multiple pockets at the front of the vest, which allow you to store food, your phone, and your blood glucose meter. This makes it easy to access your essential items while you’re running or cycling.
Glucose gelsMy general rule of thumb is bring at least twice the amount you expect you will need.
Cliff barThe cliff bar was a new addition to my nutrition. These bars seemed to work well to stabilise blood glucose and I required no additional carbs for most runs between 14-18km.
Blood glucose meter + extra stripsIf my sensor were to fail or I was to become dehydrated enough that my CGM reading was inaccurate I wanted to be able to assess my blood glucose.
DexcomContinuous glucose monitor. I ensured this had at least 24 hours to settle before the race. This way readings would more accurate.
Android APS phone The phone that contain my artificial pancreas system.
Onmipod DashBluetooth enabled insulin pump, allowing me to use Android APS. I ensured that I inserted the pod at least a day before the race so I had enough time to identify issues.
Brooks Ghost shoesA comfortable pair of shoes you have tested and run in prior to the race. I still developed blisters so its imperative you get the correct size.
Asics running socksA comfortable pair of socks.
HatA hat to ensure I don’t burn.
earbudsTo enjoy some music while I run.

Training

To prepare for the Gold Coast Marathon I did the following exercise;

Exerciser TypeCountDistanceHourAverage heart ratecoefficient of variation (%)Average blood glucoseAverage time in range
Run4127630150 bpm | 2.6 z9.68 6.680%
WeightTraining105109 bpm6.2684%
EBikeRide81428134 bpm156.873%

Continuous Glucose Monitoring (CGM) vs. Traditional Blood Testing

06/06/2023

What is CGM?

Continuous glucose monitoring (CGM) is a technology that allows people with diabetes to track their blood sugar levels in real time. A CGM sensor is inserted under the skin and measures glucose levels in the interstitial fluid, which is the fluid that surrounds the cells. The sensor sends readings to a receiver or smartphone every few minutes, so you can see how your blood sugar levels are changing throughout the day.

What is traditional blood glucose testing?

Traditional blood glucose testing involves pricking your finger to draw a drop of blood, which is then applied to a test strip. The test strip is inserted into a blood glucose meter, which provides a reading of your blood sugar level. Traditional blood glucose testing is typically done several times a day, but it can be more frequent if you have diabetes that is not well controlled.

Advantages of CGM

CGM has several advantages over traditional blood glucose testing, including:

  • Real-time monitoring: CGM allows you to see your blood sugar levels changing throughout the day, which can help you make better decisions about insulin dosing and food choices.
  • More data: CGM provides more data about your blood sugar levels than traditional blood glucose testing. This data can be used to identify trends and patterns in your blood sugar levels, which can help you improve your diabetes management and has allowed for advances like Artificial pancreas systems (APS) to be created.
  • Less finger pricks: CGM can help you reduce the number of finger pricks you need to do each day. This can be helpful for people who have diabetes and are sensitive to pain.

Disadvantages of CGM

CGM also has some disadvantages, including:

  • Cost: CGM devices can be expensive, and the sensors need to be replaced every 7-10 days.
  • Accuracy: CGM sensors are not always accurate, and they can be affected by factors such as exercise, illness, and food.
  • Inconvenience: CGM sensors can be uncomfortable to wear, and they can be damaged if they are not properly cared for.

When to use CGM

CGM is a good option for people with diabetes who want to improve their diabetes management. It is especially helpful for people who:

  • Have frequent highs and lows
  • Have difficulty controlling their blood sugar levels with traditional blood glucose testing
  • Are at risk for hypoglycemia or hyperglycemia
  • Are pregnant

What happens when you are dehydrated or playing sports?

When you are dehydrated, your blood sugar levels can rise. This is because your body is not able to get enough water to flush out excess glucose. When you are playing sports, your blood sugar levels can also rise. This is because your body is using more energy, which can lead to a release of stored glucose.

If you are using a CGM, it is important to monitor your blood sugar levels closely when you are dehydrated or playing sports. You may need to adjust your insulin dose or eat more carbohydrates to keep your blood sugar levels in a safe range.

Dexcom sensor settling time

The Dexcom sensor needs about 24 hours to settle after it is inserted. During this time, the sensor may be less accurate. It is important to monitor your blood sugar levels closely during this time and to use a backup method of blood sugar testing, such as a finger prick, if you are concerned about your blood sugar levels.

Sensor placement

The placement of the Dexcom sensor is important. The sensor should be placed on the abdomen or the back of the upper arm. It is important to avoid placing the sensor on areas of the skin that are:

  • Injured
  • Irritated
  • Tattooed
  • Scarred

Acceptable tolerance of CGMS and blood sugar testers

CGMS devices are not always accurate, and they can be affected by factors such as exercise, illness, and food. Dexcom accepts a tolerance of 20% from blood readings. This means that a CGM reading that is 20% higher or lower than a blood reading is still considered to be accurate.

Most finger prick testers can be different to laboratory results. This is because finger prick testers measure blood sugar levels in the blood, while laboratory results measure blood sugar levels in plasma. Plasma is a thicker fluid that contains more glucose than blood. This is why laboratory results are typically higher than finger prick results.

Conclusion

CGM is a valuable tool for people with diabetes. It can help you improve your diabetes management and reduce the risk of complications. If you are considering using a CGM, talk to your doctor about the best option for you.

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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.

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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%
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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).

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Thirty day challenge – week 3

Summary

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

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

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

Body Metrics

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

Exercise

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

Nutrition

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

Diabetes

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

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

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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

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).