It’s been just over a year since I permanently switched to the Omnipod, and my experience has been a blend of highs and lows. Despite a slight increase in my A1C from 5.7% to 6%, in part due to increased carbohydrate consumption, I appreciate the benefits of the Omnipod’s tubeless design. Not having to deal with tubes and the convenience of the Omnipod’s form factor have made managing my diabetes a bit less intrusive.
However, the transition hasn’t been without challenges. The Omnipod system requires immediate activation upon insertion, which can cause insulin resistance due to the initial trauma of insertion. Its design also means that you experience any issues with insulin resistance or site trauma and need to change a pump early, it can be a costly endeavour. This resistance is difficult to manage, especially around meal times when precise insulin delivery is crucial.
Another problem I encountered was tunnelling, where insulin leaks out from the cannula site. This not only affects insulin delivery but can also cause irritation. The excipient nicotinamide in the insulin formulation has also caused some site reactions for me so I mix insulin with a 50-50 ratio with Humalog. Please note this is off label.
Tips and Tricks I’ve Learned
Despite these challenges, I’ve discovered several strategies to improve my experience with the Omnipod:
Adjusting Insulin Profiles:
When installing a new pump, I set my profile to 120% to counteract any initial insulin resistance.
I try to install the pump a few hours before or after a meal to avoid the insulin resistance coinciding with a meal.
If I miscalculate the timing and need to change a pump around meal times, a short 5-10 minute walk on the treadmill helps improve insulin absorption.
Securing the Cannula:
Using Opsite Flexifix under the pump has been a game-changer. It keeps the cannula in place and reduces the need to replace the pump after activities like running.
For additional security, I use Smith+Nephew Primaflex Plus or Fixomull stretch over the pump. I use an old pump or the over-tape provided by Dexcom as a template to cut pieces as needed.
These tips have significantly improved my experience with the Omnipod, making blood sugar management more consistent and reducing the frequency of pump replacements due to physical activity.
Interesting Facts and Supporting Information
A1C and Diabetes Management: An A1C level of 6% is considered good diabetes control. According to the American Diabetes Association, an A1C below 7% is recommended for most adults with diabetes .
Insulin Absorption: The angle and method of insulin delivery can impact absorption. Studies have shown that the angle of insertion can affect how well insulin is absorbed, with certain angles potentially causing more issues like tunneling .
Use of Adhesives: Using adhesives like Opsite Flexifix can help secure insulin pumps, reducing the risk of dislodgement and improving insulin delivery reliability .
In conclusion, while my journey with the Omnipod has had its ups and downs, the freedom from tubes and the ability to manage my diabetes with less visible technology are significant benefits. With the right strategies, I’ve been able to mitigate some of the challenges and maintain effective blood sugar control.
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.
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.
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
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
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).
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.
I decided to try using an automation to lower insulin levels and raise my glucose target before doing cardio. This allows AAPS to start this process at 05:30am on my days of choice so that my body is ready to exercise safely and with less need to consume carbohydrates. In my limited testing the process is working well, with some slight tweaking for testing parameters needed. NOTE: I only added half the amount of carbs I consumed to the APS for tracking. This is to avoid overcorrecting by the algorithm.
Expectations
I am trying to find the ideal conditions to exercise where I can experience moderate blood glucose fluctuations and not be required to consume large amounts of carbohydrates to keep me exercising safely. In the past on MDI I used to exercise fasted with only basal on board, which allowed me to stay in range for about 40 minutes before needing carbs. I am hoping to achieve this same amount using a pump. In past experiments I was able to achieve similar results during exercise by significantly reducing basal rates but I found that post exercise I struggled with higher than usual blood glucose readings for a few hours due to lack of insulin in my body.
Automations
Blood Glucose vs. CGM
The CGM results differed during exercise an average of 25% from blood readings. This made me decide to start some research of my own into using machine learning to try and estimate my blood glucose during exercise.
Results / observations
The automation route works well if you plan your exercise far enough ahead. The next experiment I will drop the profile percentage to 60% and observe. I noted an average of about 25% difference between the results the CGM and the finger pick tests. I was however able to keep my readings in range 100% of the time using 34g of carbs for the duration of the 50 minute experiment.
Capture from Nightscout
Video
I created a video using data from my Garmin Forerunner 245 and AAPS to track the experiment. In this video I track blood glucose, insulin, carbs, basal, distance, heart rate and cadence. I noted that the algorithm the Garmin uses to determine distance does not work well while walking and didn’t register any distance until I started lightly jogging.
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.
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.
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.
The below is a list of foods I commonly eat that I decided to analyse. I have listed some lower-carb high protein meals, snacks and a few higher-carb meals. These are all more regular meals so I have had the opportunity to adjust my insulin strategy to better cover the various aspects of the meal.
Part of my strategy is to adjust the insulin timing (pre-bolus), starting BG (using temp targets or exercise) and the amounts of insulin used. The amount of insulin used needs to be adjusted based on the macronutrient composition, the amount of fibre and the amount of sugar alcohol in your meal. Sugar alcohols are often not listed on food labels in Australis and those foods should be avoided. Read my post on injecting for protein and fat for a better understanding of how your insulin dose will need to be adjusted to compensate for protein and fat.
To analyse a meal I use three (3) values, standard deviation, time in range (TIR) and Coefficient of the variation. These three (3) values will assist you in determining how good or bad a meal was for you in terms of blood sugar impact (BGI). You can also use the difference between the 3 hour and 5 hour standard deviations to ascertain if you are covering gluconeogenesis correctly.
Time in Range (TIR): For TIR we are looking for a high percentage of your readings within a normal (I use 3.9-7.8 mmol/l ) range.
Standard Deviation: For standard deviation I look for values under 1 as a meal that has little to no blood glucose impact (BGI).
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. The lower the number the better the outcome.
On the 04 February 2022 I decided to switch from FreeAPS (Loop) over to AAPS. At that point I had been successfully looping for 6 months with FreeAPS (Loop) but I wanted to get some experience with the oref1 algorithm in the hopes of fine-tuning my diabetes management during sports and to try out unannounced meals.
I was conflicted in the beginning as I was seeing results consistent with my goals on FreeAPS (Loop), but It was something I wanted to try. I am glad that I did.
The first few weeks on AAPS can be painful as you learn the system and go through the objectives. I had completed as many as I could using a virtual pump so It wasn’t long before I was able to close the loop again this time using AAPS. I expect my numbers will improve once again when automations are available to me.
Setup and Configuration:
For the first few days I was still using the Dexcom IOS app as my collector app sending my BG readings to the King King Mini 2 (KKM2) to be processed by AAPS, this worked well as I was always in areas with reception. I did this because I wanted to be able to switch back to Free-APS quickly if I decided that AAPS wasn’t working for me. The first sensor on the KKM2 I paired with xDrip+. I loved xDrip as it provided heaps of additional data, but I had issues with delayed and missed readings. I have now switched to the the Dexcom BYOD app and this seems to be proving readings more consistently. I am using an Anubis transmitter but I am unable to validate battery level with the Anubis Tool on the KKM2.
Stats:
Last 22 days on Loop (FreeAPS)
…
First 21 days on AAPS
Likes:
Improved Time-in-range (TIR) (+7.7 %), average blood glucose (-0.2 mmol/l), GVI, PGS and A1C even though AAPS was new to me and I was still figuring a few things out. I achieved this exercising less than I usually do due to weather.
Better control with less work.
More flexibility – The ability to scale and tailor your meal or correction dose is awesome (include trend, IOB, COB, correction percentage and your blood glucose readings in the the calculation for increased control and precision).
Fewer (0.2% less) low events.
Quicker to respond to bring high blood sugars down.
Unannounced meal management (UAM) using the Oref1 algorithm (not tested).
No Apple Developer licence fee (I paid less for my KKM2 than I was paying for my annual developer license).
Easier to setup and deploy to the KKM2 than the Free-APS (Loop) app.
Remote (SMS) bolus.
The ability to super bolus (include basal for a specific period with a bolus).
Super micro bolus’ (SMB) are more effective at dealing with gluconeogenesis from high protein meals.
Autosens has been useful by identifying periods of insulin resistance or sensitivity and adjusting basal accordingly.
Dislikes:
My pump (Medtronic 522) is using batteries more frequently (60% quicker on AAPS).
Bluetooth (BT) drop-outs more frequently than loop. In the last 21 days I have had 6 ‘Pump unreachable’ errors and 3 ‘Missed BG readings’. This resulted in elevated blood glucose during the evening.
The KKM2 battery drains faster when I am around multiple other Bluetooth enabled devices than what the iPhone did.
The connection between the Phone (King Kong Mini 2) to the Orangelink and pump seems a little less stable than with the with the iPhone and loop, but I suppose you can expect a far inferior Bluetooth chip on a phone that costs a 10th of the price. This is easily remedied by restarting the Orangelink , turning BT on and off or in some cases, usually with the the pump unreachable error, I had to restart my phone.
I really liked the ICE (Insulin Counteraction effect) data in Loop. It was useful to see where I went wrong with my previous bolus and AAPS doesn’t have this data readily available like Loop did. If you were using UAM it would be unnecessary.
It’s been 73 days since I started looping. I have had a difficult December with a sprained wrist from a mountain bike accident and myself, my daughter and my wife had gastro which resulted in very little sleep and some abnormal readings. In fact I am still having abnormal sensitivity to insulin, resulting in frequent lows or blood sugar swings. I also had two failed Dexcom sensors and moved to the code calibration method which resulted in two days of false high CGM readings in comparison to my blood glucose readings. I’ll add the CGM stats once I am finished the analysis. Hello 2022!
Blood Glucose Stats
Blood glucose stats
A marginal improvement in December over the fist two months, but I still have a lot of work to do to get to my goal of a 5.5% A1C. Interestingly enough, after the gastro I am now 40% more sensitive to insulin, so hopefully now that I am aware of this I can get back to better blood glucose readings. I will also need to run in open-loop when changing Dexcom sensors to avoid all the issues I was previously having with false high blood glucose readings causing my Loop to micro-bolus incorrectly or increase my basal in error. Still not sure how I will handle protein in open-loop.
Exercise Stats 2021-2015
Total distance exercised
Total time spend exercising
I more than doubled (55%) the amount of hours I spent exercising in 2021, mostly as I was working from home which allowed me to spend more time exercising. As my A1C lowered, my fitness levels improved dramatically.
Dick The Diabetic's APS Adventures 