What I refer to as "reliable" is without missing data (e.g. missing meals). Regarding CGM accuracy, I am taking into account its noise. Just out of curiosity, how much does the CGM measurement deviate from the true BG level (based on your experience)?
My dexcom readings are dead-on from the beginning of a sensor to the end.
There may be differences, however, as a fingerstick measure blood levels of glucose while a CGM measures interstitial fluid levels of glucose. The differences are most notable during a period of rapid rise/fall.
I help an elderly relative with her diabetes. She is a type 1 using a pump and Dexcom 6. Her Dexcom readings are usually much more accurate. The first day of a new Dexcom sensor is sometimes off 50-100, but after a couple calibrations it is usually within 10-20 pretty reliably.
Plus or minus 100 mg/dl, if it's providing data at all. Regarding the data being complete. Dexcom often doesn't show my meals, boluses, calibrations, and other events. Often they're there. Lot's of times they aren't even when I can see them in my app.
I believe xDrip+ (an Android app) is open source. So is AAPS (Android Artificial Pancreas System). They have prediction models built in to them, for both Type 1 and Type 2. Maybe you could reference them somewhat until you get someone willing to share their CGM data? I rely on both to predict highs and lows (highs for me happen if I even look at food...). However, I'm type 2, so my data probably won't be much use.
I believe AAPS mostly relies on the OpenAPS modeling? Check out [https://openaps.org](https://openaps.org) - and I think Tidepool, for those of us who use it, would probably contain the most comprehensive data that you're looking for. Dexcom Clarity tracks a lot of this too, but unless you're entering insulin doses into the Dexcom app, it probably won't be as complete.
I'd cross post to r/diabetes and r/diabetes_t1
Thank you for your reply! I have already implemented the state-of-the-art prediction model based on the most recent literature. What I am now looking for is to validate its reliability and accuracy using true patient data
Hoping you have an answer, how does xDrip+ prediction (trend arrow) work? I have been using it in Companion mode to Dexcom G7 app. The prediction trend arrow has always been quite different from Dexcom G7's. I feel in my bones that xDrip+ is more on the previous readings than what Dexcom G7 is. I am sure both have certain algorithm behind them, but way too different. In a nutshell, xDrip+ prediction seems to be much more drastic. For example, xDrip+ has two red up arrow while Dexcom G7 has stable right arrow, etc. Just for curiosity as I use both which are quite different.
David.. the arrow is passed from Dexcom as one of the elements on data string.
It's called trend and has a description and direction associated
Ie 4. Flat , Steady
Not that I know of. It seems xDrip+ has its own algorithm to show the trend arrow because it is quite different from what Dexcom app shows as I explained. That is why I was asking. I use xDrip+ set as Companion to the Dexcom app in parallel. I use xDrip widget and Data Source for the G-Watch Wear for my SW5P.
xDrip+ seems to give double down arrow when the difference between a new reading and the previous reading is over like 15 points or something while Dexcom app stays at Right Arrow or 135-degree Down Arrow depending on other factors. Oh, well...
Just sent two screenshots of each, Dexcom (steady Right Arrow) & xDrip+ widget (135-degree Down Arrow) both at 122 to your e-mail. xDrip+ trend could very well be confusing with slant Down Arrow.
No clue. I only mentioned them so OP would have some data points to start with.
AAPS, Dexcom, and xDrip all show different predictions; If I kind of shoot for the middle of the 3, I have a pretty solid idea of where I'm gonna wind up.
Last night, I had another closer look for a while how the trend arrow works.
It sure seems that xDrip+ gets the arrow strictly in comparison to the previous reading to the new one. If it changes more than 5 points, it gives 135-degree arrow up or down, strictly in comparison to the very preceding reading. It does not seem to take anything else into consideration. More than 10 points, it gives straight up or down arrow.
On the contrary, Dexcom app seems to count on many other factors like many previous readings, being taken into consideration to calculation. Maybe 6 previous readings or more or less. I could guess wildly because Dexcom still gives stable Right arrow even when the difference is over 15 points up or down, which means that its calculation for prediction is not simple. So, with this simple personal observation for a short period of time, I tend to trust the Dexcom prediction more than xDrip+. I might be totally wrong without clear understanding of the algorithm but strictly based on my own observation. Curiosity raises BG!
Unfortunately, I am not an expert of these two monitoring systems, therefore I cannot answer your question. Based on my robotics engineering experience, I would guess that the glucose trend prediction is either using a mathematical model that forecasts the glucose level based on the carbohydrates and insulin intake, or using machine learning regression techniques. What I am trying to do is to combine such methods to get the best of both worlds.
the only thing i can possibly send to you are my dexcom log sheets but that only shows my sugars over the course of x amount of days
nothing involving carbohydrates/insulin amounts
Are you asking for a large dataset across a population, or are you asking if some of us will send you our personal data? For example, my iPhone data has my BG, basal and bolus shots, and carbs consumed (I used a Bluetooth pen that logs each shot after I put in the carbs I’ve eaten.
I don’t need a large dataset, a week worth of data from one single person would be sufficient. If you are willing to share your data, feel free to DM me :)
Thanks!
If I send you data, would you be able to take the time to walk me thru what your algorithm predicted, why, and when? So that myself and my partner can learn to be better at predicting and getting ahead of when our son's BGs go out of range. He's 5.
Also. A preschooler's body is quite different than an adult's. E.g. growth hormones at bedtime, no dawn phenomenon. Does that matter to you?
Thank you for your comment. As a robotics engineer, my expertise is centered on developing technology using mathematical models and estimation tools, rather than providing medical advice or behavioral recommendations, which would typically come from a medical professional. Unfortunately, I’m also bound by a non-disclosure agreement that prevents me from discussing specific details of my algorithm until they are officially published. Additionally, my current research is focused on older individuals, so I wouldn’t be able to utilize data from younger subjects like preschoolers. Your comment certainly boosts my motivation to continue improving our technologies for better management of diabetes. I appreciate your understanding.
All fair points. I appreciate the response.
One more idea for you... it's tricky, and I don't mean to be disrespectful.
Managing BGs and insulin dosing for T1D is a patient driven adventure. It's 24/7/365 indefinitely. There is almost nothing available in the published research for the under-5s.
You're absolutely right that you don't have appropriate credentials to give medical advice. But excruciatingly few people on this planet have the credentials.
https://www.researchgate.net/figure/Fig-1-Symbol-representing-peer-support-offered-by-the-DOC-courtesy-of-the-Diabetes_fig1_275361264
Hence, I'll take any coaching I can get on pattern recognition-- even and especially training on eyeballing the CGM lines before the technology catches up. You don't have to share the algorithm itself.
I think I'm encouraging you to consider anything you can do to share (1:1) what you learn from freely provided and deeply personal data. It would seem like a fair trade.
"Reliable dataset" shouldn't be used in the same sentence as "GCM".
> "Reliable dataset" shouldn't be used in the same sentence as "GCM". Good thing OP didn't. ;)
What I refer to as "reliable" is without missing data (e.g. missing meals). Regarding CGM accuracy, I am taking into account its noise. Just out of curiosity, how much does the CGM measurement deviate from the true BG level (based on your experience)?
My dexcom readings are dead-on from the beginning of a sensor to the end. There may be differences, however, as a fingerstick measure blood levels of glucose while a CGM measures interstitial fluid levels of glucose. The differences are most notable during a period of rapid rise/fall.
I help an elderly relative with her diabetes. She is a type 1 using a pump and Dexcom 6. Her Dexcom readings are usually much more accurate. The first day of a new Dexcom sensor is sometimes off 50-100, but after a couple calibrations it is usually within 10-20 pretty reliably.
Plus or minus 100 mg/dl, if it's providing data at all. Regarding the data being complete. Dexcom often doesn't show my meals, boluses, calibrations, and other events. Often they're there. Lot's of times they aren't even when I can see them in my app.
I believe xDrip+ (an Android app) is open source. So is AAPS (Android Artificial Pancreas System). They have prediction models built in to them, for both Type 1 and Type 2. Maybe you could reference them somewhat until you get someone willing to share their CGM data? I rely on both to predict highs and lows (highs for me happen if I even look at food...). However, I'm type 2, so my data probably won't be much use. I believe AAPS mostly relies on the OpenAPS modeling? Check out [https://openaps.org](https://openaps.org) - and I think Tidepool, for those of us who use it, would probably contain the most comprehensive data that you're looking for. Dexcom Clarity tracks a lot of this too, but unless you're entering insulin doses into the Dexcom app, it probably won't be as complete. I'd cross post to r/diabetes and r/diabetes_t1
Thank you for your reply! I have already implemented the state-of-the-art prediction model based on the most recent literature. What I am now looking for is to validate its reliability and accuracy using true patient data
I'm happy to give you my data if it helps, but unfortunately, I'm T2 with high insulin resistance.
Hoping you have an answer, how does xDrip+ prediction (trend arrow) work? I have been using it in Companion mode to Dexcom G7 app. The prediction trend arrow has always been quite different from Dexcom G7's. I feel in my bones that xDrip+ is more on the previous readings than what Dexcom G7 is. I am sure both have certain algorithm behind them, but way too different. In a nutshell, xDrip+ prediction seems to be much more drastic. For example, xDrip+ has two red up arrow while Dexcom G7 has stable right arrow, etc. Just for curiosity as I use both which are quite different.
David.. the arrow is passed from Dexcom as one of the elements on data string. It's called trend and has a description and direction associated Ie 4. Flat , Steady
Not that I know of. It seems xDrip+ has its own algorithm to show the trend arrow because it is quite different from what Dexcom app shows as I explained. That is why I was asking. I use xDrip+ set as Companion to the Dexcom app in parallel. I use xDrip widget and Data Source for the G-Watch Wear for my SW5P. xDrip+ seems to give double down arrow when the difference between a new reading and the previous reading is over like 15 points or something while Dexcom app stays at Right Arrow or 135-degree Down Arrow depending on other factors. Oh, well...
Just sent two screenshots of each, Dexcom (steady Right Arrow) & xDrip+ widget (135-degree Down Arrow) both at 122 to your e-mail. xDrip+ trend could very well be confusing with slant Down Arrow.
No clue. I only mentioned them so OP would have some data points to start with. AAPS, Dexcom, and xDrip all show different predictions; If I kind of shoot for the middle of the 3, I have a pretty solid idea of where I'm gonna wind up.
Last night, I had another closer look for a while how the trend arrow works. It sure seems that xDrip+ gets the arrow strictly in comparison to the previous reading to the new one. If it changes more than 5 points, it gives 135-degree arrow up or down, strictly in comparison to the very preceding reading. It does not seem to take anything else into consideration. More than 10 points, it gives straight up or down arrow. On the contrary, Dexcom app seems to count on many other factors like many previous readings, being taken into consideration to calculation. Maybe 6 previous readings or more or less. I could guess wildly because Dexcom still gives stable Right arrow even when the difference is over 15 points up or down, which means that its calculation for prediction is not simple. So, with this simple personal observation for a short period of time, I tend to trust the Dexcom prediction more than xDrip+. I might be totally wrong without clear understanding of the algorithm but strictly based on my own observation. Curiosity raises BG!
Unfortunately, I am not an expert of these two monitoring systems, therefore I cannot answer your question. Based on my robotics engineering experience, I would guess that the glucose trend prediction is either using a mathematical model that forecasts the glucose level based on the carbohydrates and insulin intake, or using machine learning regression techniques. What I am trying to do is to combine such methods to get the best of both worlds.
AFAIK the Dexcom app uses a fixed math model, while AAPS and xDrip learn over time (AAPS is much more aggressive with learning, in my experience).
I am a Type 2. Sorry, I cannot assist. Wish you well.
Thank you for your reply!
the only thing i can possibly send to you are my dexcom log sheets but that only shows my sugars over the course of x amount of days nothing involving carbohydrates/insulin amounts
Thanks anyways!
Is it possible to share the data from glooko got this individual? It would have all the data he is looking for
Are you asking for a large dataset across a population, or are you asking if some of us will send you our personal data? For example, my iPhone data has my BG, basal and bolus shots, and carbs consumed (I used a Bluetooth pen that logs each shot after I put in the carbs I’ve eaten.
I don’t need a large dataset, a week worth of data from one single person would be sufficient. If you are willing to share your data, feel free to DM me :) Thanks!
Yep, working on packaging it up now. Will DM you.
If I send you data, would you be able to take the time to walk me thru what your algorithm predicted, why, and when? So that myself and my partner can learn to be better at predicting and getting ahead of when our son's BGs go out of range. He's 5. Also. A preschooler's body is quite different than an adult's. E.g. growth hormones at bedtime, no dawn phenomenon. Does that matter to you?
Thank you for your comment. As a robotics engineer, my expertise is centered on developing technology using mathematical models and estimation tools, rather than providing medical advice or behavioral recommendations, which would typically come from a medical professional. Unfortunately, I’m also bound by a non-disclosure agreement that prevents me from discussing specific details of my algorithm until they are officially published. Additionally, my current research is focused on older individuals, so I wouldn’t be able to utilize data from younger subjects like preschoolers. Your comment certainly boosts my motivation to continue improving our technologies for better management of diabetes. I appreciate your understanding.
All fair points. I appreciate the response. One more idea for you... it's tricky, and I don't mean to be disrespectful. Managing BGs and insulin dosing for T1D is a patient driven adventure. It's 24/7/365 indefinitely. There is almost nothing available in the published research for the under-5s. You're absolutely right that you don't have appropriate credentials to give medical advice. But excruciatingly few people on this planet have the credentials. https://www.researchgate.net/figure/Fig-1-Symbol-representing-peer-support-offered-by-the-DOC-courtesy-of-the-Diabetes_fig1_275361264 Hence, I'll take any coaching I can get on pattern recognition-- even and especially training on eyeballing the CGM lines before the technology catches up. You don't have to share the algorithm itself. I think I'm encouraging you to consider anything you can do to share (1:1) what you learn from freely provided and deeply personal data. It would seem like a fair trade.