2024 Half Marathon

08 July 2024

The half marathon has passed. The training this year went well, with no running related injuries to speak of at the point of writing, although I did get food poisoning a week before the race and I missed my last long run. I have learned a lot over the course of the year, which has helped get me to this point. The actual race was a totally different experience, it rained for the first few kilometres, I had stomach cramps and I suffered intense muscles spasms, none of which happened in over a thousand kilometres of my training over the course of the last two (2) years.

Race Day

Race day 2024 was very different than I expected. I felt confident due to all my training. The rain was an annoyance, but one easily overcome by a running jacket (if I race again I’ll get a opaque poncho).

I woke up at 03:50am with little sleep and a blood glucose of 5.8 mmol/l. This crept up steadily, likely due to cortisol and adrenaline. By race start time I was 9.0 mmol/l with 0.9 units of insulin on board (IOB). Due to the IOB I ate about one third of a Cliff Bar (18g of carbohydrates) which in hind-sight was a mistake.

Due to the inclement weather my Garmin didn’t pick up my heart rate on my watch consistency, or perhaps even accurately. I found my Garmin advising I was running at approximately 130 BPM even though I felt I was pushing quite hard. I got a personal best (61 minutes) for the first 10 kilometres.

Label	Race Day	Average during Training
Start Time	06:23 am
Distance	21.2km
Average HR	133 BPM
Standard Deviation	2.3 mmol/l	0.8
Coefficient of the variation	31.9%	11.3%
Blood Glucose – start	9 mmol/l	6.5
Blood Glucose – min	4.4 mmol/l
Blood Glucose – max	11.1 mmol
Blood Glucose – average	7.4 mmol/l	6.7
Time in Range (3.9-7.8)	51%	71.9%
Insulin on board	0.99	0.1

Race day compared to training was wildly different, I will need to analyse the data and come up with a better race day strategy.

Time vs. Pace with a Stamina and Blood sugar overlay.

Time vs. Heart Rate with a Pace and Blood sugar overlay.

Training

I’ll add the link once all data is processed.

Equipment

I try to come prepared for all possibilities.

This year I spend a lot of time finding the perfect shoe for my unique requirements, namely that I supinate on my right foot due to an atrophied right calf muscle. In my testing, the Brooks Ghost performed the best, allowing me to run any distance with no pain or discomfort.

Final Thoughts

The food poisoning caused an electrolyte balance, which resulted in muscle cramps on race day. This was something I had not experienced during my training, an I was ill prepared for it.

My sugars were higher than during training again, and if I do this again I will refrain from coffee or any carbs prior to the event.

Time in Tighter Range (TITR): A Powerful Metric for Measuring Diabetes Control

Introduction

For individuals living with diabetes, maintaining stable blood glucose levels is a critical aspect of managing their condition effectively. Traditionally, glycated hemoglobin (HbA1c) has been the primary metric for evaluating long-term glucose control. However, it provides only a snapshot of average glucose levels over several months. To gain deeper insights into daily glycemic patterns and fluctuations, healthcare professionals and patients are turning to a more comprehensive and dynamic metric called “Time in Tighter Range” (TITR).

What is Time in Tighter Range (TITR)?

Time in Tighter Range (TITR) is a metric that quantifies the percentage of time blood glucose levels remain within a specific target range. The target range is often defined as the optimal window where glucose levels are considered both safe and effective in reducing the risk of diabetes-related complications. Commonly, the TITR target range is set between 70-140 mg/dL (3.9-7.8 mmol/L), but it can be tailored to an individual’s needs based on age, health status, and treatment goals.

Why TIR Matters in Diabetes Management

Real-Time Assessment: Unlike HbA1c, which provides a retrospective average, TIR offers real-time data, empowering patients and healthcare professionals to make immediate adjustments to diabetes management strategies.
Insights into Glucose Patterns: TITR helps reveal patterns and trends in glucose control, identifying potential trouble spots and offering opportunities for targeted interventions.
Reduction of Hypoglycemia and Hyperglycemia: Maintaining TIR within the target range can reduce both hypoglycemic episodes (dangerously low blood glucose levels) and hyperglycemia (elevated blood glucose levels), enhancing overall quality of life and mitigating diabetes-related complications.

Tracking and Monitoring with TITR

Using TIR involves continuous glucose monitoring (CGM) or frequent blood glucose measurements. The data is then analyzed to determine the percentage of time spent within the target range. Several approaches can be used to track TIR:

CGM Devices: Advanced CGM devices automatically calculate and display TIR data, offering users real-time feedback on their glucose control.
Data Logs: Patients and healthcare professionals can manually record blood glucose readings and calculate TIR using spreadsheets or dedicated apps.

TITR in Real-Life Scenarios

Personalized Diabetes Management: TITR allows for a tailored approach to diabetes management. It helps healthcare professionals customize treatment plans and make timely adjustments to insulin dosing, diet, and exercise regimens.
Pregnancy and Diabetes: During pregnancy, TITR is critical for expectant mothers with diabetes, as tight glucose control is vital for the health of both the mother and the baby.
Sports and Physical Activity: For athletes with diabetes, TITR provides insights into glucose fluctuations during physical activity, helping them optimize performance and avoid glucose-related issues during exercise.

Conclusion

Time in Tighter Range (TITR) is a valuable and dynamic metric that goes beyond traditional HbA1c measurements, providing real-time insights into daily glycemic patterns. With its ability to track fluctuations and trends within the target range, TITR empowers individuals and healthcare professionals to take proactive steps towards better diabetes management. By striving for optimal TITR, patients can enhance their quality of life, reduce the risk of complications, and achieve greater control over their diabetes. As TITR continues to gain prominence in diabetes care, it offers new possibilities for personalized and effective diabetes management strategies.

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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.

Metric	Non-diabetic	Last two (2) week*	Last 3 months	Last year	Boost	AIMI AI
eHbA1c	5.1%	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)	5.5	6.2	6.5	6.5	6.5	6.5

*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/

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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. Race	Average distance (km)	Average time (min)	Average HR (bpm)	TIR (3.9 – 7.8)	Average Blood Glucose (mmol/l)	Coefficient of variation (%)	Pace
Training	14	92	156	92%	5.8	14	6:40
Race	21.4	140	151	21.4%	9.9	22.54	6:39

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:

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.

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.

Managing Blood Glucose During the Race: Once the race begins, various factors can influence your blood glucose levels.

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:

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 Name	Note
Osprey duro 6 – Hydration pack	This 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 gels	My general rule of thumb is bring at least twice the amount you expect you will need.
Cliff bar	The 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 strips	If 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.
Dexcom	Continuous 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 Dash	Bluetooth 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 shoes	A 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 socks	A comfortable pair of socks.
Hat	A hat to ensure I don’t burn.
earbuds	To enjoy some music while I run.

Training

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

Exerciser Type	Count	Distance	Hour	Average heart rate	coefficient of variation (%)	Average blood glucose	Average time in range
Run	41	276	30	150 bpm \| 2.6 z	9.68	6.6	80%
WeightTraining	10		5	109 bpm	6.2	6	84%
EBikeRide	8	142	8	134 bpm	15	6.8	73%

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.

CGMs vs. Traditional Blood Testers: Revolutionizing Glucose Monitoring

05/06/2023

Introduction:
Monitoring blood glucose levels is a vital aspect of managing diabetes, as it helps individuals make informed decisions about their diet, insulin dosage, and overall health. For many years, traditional blood testers were the primary method of measuring glucose levels. However, with advancements in technology, continuous glucose monitors (CGMs) have emerged as a game-changer in diabetes management. In this blog post, we will explore the key differences between CGMs and traditional blood testers, delve into the effects of dehydration and sports activities on glucose readings, and touch upon the settling time required for CGM sensors like Dexcom.

CGMs vs. Traditional Blood Testers: An Overview:
Traditional blood testers, commonly known as fingerstick glucose meters, require a small blood sample obtained by pricking the finger with a lancet. The sample is then placed on a test strip, which is inserted into the meter for analysis. This process provides a snapshot of the blood glucose level at the specific moment the test is performed. It requires periodic testing throughout the day to get an idea of how glucose levels fluctuate.

On the other hand, CGMs provide continuous and real-time glucose readings throughout the day without the need for fingerstick tests. CGMs consist of a small sensor inserted under the skin, which measures interstitial fluid glucose levels, usually every few minutes. The data collected is transmitted wirelessly to a receiver or a smartphone app, allowing users to monitor their glucose levels continuously and detect trends and patterns.

The Benefits of CGMs:

Continuous Monitoring: CGMs offer a comprehensive view of glucose levels, revealing trends, highs, and lows that might be missed with traditional blood testers.
Alerts and Alarms: CGMs can be set to provide notifications when glucose levels fall outside of a target range, helping individuals take immediate action and avoid severe hypo- or hyperglycemia.
Data Analysis: CGMs generate detailed reports and graphs, enabling healthcare providers to analyze glucose patterns over extended periods, leading to more informed treatment decisions.

Dehydration and Sports: Implications for Glucose Monitoring:
Dehydration and engaging in physical activities such as sports can affect glucose readings. When dehydrated, the blood becomes more concentrated, leading to a higher glucose concentration in the blood. Consequently, both CGMs and traditional blood testers may yield elevated glucose readings in dehydrated individuals. Therefore, it is crucial to stay adequately hydrated to ensure accurate glucose measurements.

During sports or rigorous exercise, the body’s demand for energy increases, resulting in the release of stored glucose. This can lead to a temporary decrease in glucose levels. CGMs, with their continuous monitoring capabilities, can help individuals track these fluctuations in real-time and take necessary steps to prevent hypoglycemia.

Sensor Settling Time: Dexcom and the 24-Hour Period:
Dexcom, one of the leading manufacturers of CGMs, suggests a 24-hour settling period for their sensors. This recommendation accounts for the initial trauma caused by sensor insertion. During this period, users may experience inaccurate readings or fluctuations. Waiting for the sensor to settle allows for stabilization and more reliable glucose measurements.

Conclusion:
The advent of CGMs has revolutionized glucose monitoring, offering substantial benefits over traditional blood testers. With continuous monitoring, alerts, and data analysis capabilities, CGMs empower individuals with diabetes to make more informed decisions about their health. However, it is important to stay hydrated and consider the effects of physical activities on glucose readings. Furthermore, users of CGMs like Dexcom should allow for a 24-hour settling period to ensure accurate and reliable measurements. Embracing this technological advancement can significantly enhance the management of diabetes, promoting better health outcomes for individuals worldwide.

References:

American Diabetes Association.

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2023 Half Marathon

3rd Apr 2023

It’s been a long-standing goal of mine to run a half marathon. It’s long enough to be a challenge, and short enough that I don’t need to be training all year round and can focus on my other sports.

Pre-requisites

Basal review – I will be doing an incremental basal review in the next few days (hopefully). Skipping meals where required.

Full profile review – Once the basal profile has been updated, I will check my CR (carb ratio) and CRR (carb rise ratio). No need to check ISF (insulin sensitivity factor) as its calculated in Android APS. I will need to be on the look out for blood sugar dips two or more hours after meals as I may need to reduce the Dynamic ISF Adjustment factor.

Injuries

At the moment I have an Achilles tendon issue I am in rehabilitating. It’s the first time I am experiencing this issue, so I am working with a Physio to remedy it.

Training Program

I plan on using the Garmin training program to do most of my training. My longest run prior to this was 16 km and I mountain bike so I think I may be ok with fitness if I can get back into training fairly quickly, but this is dependent on how well my current rehab program works.

This will be updated as and when I can, but the next 3 three (3) weeks are as follows:

Tendon Rehab Program:

Week	Monday	Tuesday	Wednesday	Thursday	Friday	Saturday	Sunday
1	Calf raise holds 5 x 45 seconds, Gym	3km run, double leg calf raise x 3 12-15, body weight single leg calf raise 3 x 10-15	Calf raise holds 5 x 45 seconds, Gym	Calf raise holds 5 x 45 seconds, Gym	3km run in AM, double leg calf raise x 3 12-15, body weight single leg calf raise 3 x 10-15	Calf raise holds 5 x 45 seconds, Gym	Bike in AM
2	Calf raise holds 5 x 45 seconds, Gym	4-5 km	Calf raise holds 5 x 45 seconds, Gym	Calf raise holds 5 x 45 seconds, Gym	4-5 km	Calf raise holds 5 x 45 seconds, Gym	Bike in AM
3	Calf raise holds 5 x 45 seconds, Gym	5-7km	Calf raise holds 5 x 45 seconds, Gym	Calf raise holds 5 x 45 seconds, Gym	5-7km	Calf raise holds 5 x 45 seconds, Gym	Bike in AM

NOTES: If pain/stiffness gets progressively worse, then reduce load and re-assess. If not monitor and keep working.

Strava Running Program:

I had really wanted to use the Garmin program, but I was too late to start it. The Strava program doesn’t seem to have the ability to select the days I plan on running or feedback on training progress at a granular level. My desired routine is 3 days per week.

Garmin Running:

Global Triathlon Network (GTN) half marathon training program

I really liked the plan from GTN, I have modified it a little to fit within my availability.

Click to access GTN-HALF-PLAN.pdf

Training Progress

I will add a table to the weekly updates with progress on my training plans.

Diet / Food

I plan on sticking to my diet as much as possible. I will however cut back on alcohol and focus on drinking more water.

Supplements

Vitamin B – https://www.healthline.com/health/food-nutrition/vitamin-b-complex#benefits

Alpha lipoic acid – https://www.healthline.com/nutrition/alpha-lipoic-acid

Omega 3 – https://www.healthline.com/nutrition/17-health-benefits-of-omega-3

Vitamin D – https://www.healthline.com/health/food-nutrition/benefits-vitamin-d

Gear

Shoes: New Balance 1080, Fresh Foam More v3, Brooks Ghost

Watch: Garmin Fenix 7

Hydration vest: Osprey Duro 6 hydration vest

APS Hardware: Cubot King Kong Mini 2 Pro

Artificial Pancreas System: Android APS / Branch: Dev (Dynamic ISF)

Pump: Mixture of Omnipod and Accu-Check Combo

Insulin: Fiasp

Insulin Peak: 55 minutes

DIA: 9 hours

Glucose statistics

Measurements

Weight: 75km (afternoon)

Waist: 88cm

Body fat (estimate):

Updates (Weekly)

I will try and update the blog weekly with progress.

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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 🙂

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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

	Start	Week 1	Week 2	Week 3	Week 4
Weight (kilograms)	75.8	74	75	74.1	73.1
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 1	Week 2	Week 3	Week 4
Distance (kilometres)	25.17	20.54	37.22	29.4
Activity (hours)	4.34	3.65	5.64	5.4

Table stating the weekly exercise metrics I am tracking

Nutrition

Screenshot of average macro-nutrients consumed during week 4

Diabetes

	Week 1	Week 2	Week 3	Week 4
Low (<3.9) (%)	0.9	0.6	3.5	1.6
In Range (3.9-7.8) (%)	75.3	74.7	71.9	78.9
High (>= 7.8) (%)	23.8	24.7	24.6	19.5
Standard deviation (SD)	1.3	1.7	1.7	1.5
Average (mmol/l)	6.8	7.0	6.7	6.5
A1c estimation (%)	5.9	6.0	5.8	5.7

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).