Quest Protein Bar T1D Review

Is the Quest protein bar type 1 diabetic friendly? Yes I think it is, read more below to discover why I think it is.

Review

Nutritional Information
Insulin Strategy
Goal
Results

When trying anything new I always read the nutritional information on order to determine the impact it will have on my body. Certain high fat foods can cause insulin resistance and inflammation and will delay gastric emptying while protein will digest and get synthesised into carbohydrates.

Below are two great resources you should read before deciding on your final dosing strategy. Its important to note that in Australia, most items don’t have total carbohydrate count that includes fibre and sugar alcohol, which can make it difficult to assess the impact of products that don’t list sugar alcohols in the nutritional information.

Net Carbs Vs. Total Carbs: What Counts?

Counting Sugar Alcohols

Insulin Strategy

Based on the nutritional information above, my inulin to carb ratio and my proximity to recent exercise I decided to inject as follows; I didn’t input my eCarbs for the protein as I knew that AAPS would be able to manage. Read my post for injecting for protein and fat if you are not on an AAPS or experience elevated blood glucose two (2) hours after eating.

Goal

The goal of any insulin strategy would be to inject enough insulin at the correct time so that the upward force the carbohydrates exert is counteracted by the downward force the insulin exerts and you stay in range for the duration of the meal.

To analyse this I use three (3) values, standard deviation, time in range (TIR) and Coefficient of the variation. These three (3) values will assist you in determining how good or bad a meal was for you in terms of blood sugar impact (BGI).

Time in Range (TIR): For TIR we are looking for a high percentage of your readings within a normal (I use 3.9-7.8 mmol/l ) range.

Standard Deviation: For standard deviation I look for values under 1 as a meal that has little to no blood glucose impact (BGI).

Coefficient of the variation (CV): Is the standard deviation divided by the average glucose. Its a measure that helps normalise the results by reducing the influence on average glucose. Most studies indicate that anything under 33% is good.

Picture Source: See my CGM. https://seemycgm.com/2017/08/09/why-dia-matters/

Results

As we can see by the table below that this snack consumed with the correct insulin strategy resulted in very stable blood glucose over a number of hours, with little deviation. What should be noted is that the sugar alcohol started to effect readings after 3 hours and that 1 hour prior to consumption I had exercised. The exercise would have increased my insulin sensitivity.

Time in Range (TIR): 100%

Standard Deviation: 0.38

Coefficient of the variation (CV): 0.06

Read my post on some common foods I eat to gain a better understanding of how this meal impacted me in comparison.

Food Information table

8th Mar 2022

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.

Equipment/ Software: Dexcom G6, Android APS, Medtronic 522, OrangeLink Pro, Nightscout, Python, Excel

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).

Loop (FreeAPS) to Android APS (AAPS) – 3 Week Review

2nd Mar 2022

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:

…

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.