Researchers design wearable tech that can sense glucose levels more accurately

Type 1 here, a 15 min lag is fine. The constant sampling especially overnight and the multiday graphs are what I love about CGM. I've always been in decent but loose control 6.7 to 7.1 A1C (longer term measurement). After a year w CGM I got to 6.5. Now last checkup I'm at 5.9, this ties my record from my first month on Lantus insulin, never repeated in 19 years until now. Also getting numbers on phone and checking number every 15 min while driving are amazing. The stock software for Dexcom and Freestyle are both abysmal. Both refuse to allow silent mode, Dexcom has a hard 6 hours left on sensor uninteruptable alarm, woke me up at 4 am, coustomer service had nothing to say, so Goodbye. I went back to Freestyle their handheld reader is silencable and I use a third party app called Juggluco for my Android phone. Sorry I've survived 42 years on human insulin I don't want software taking over my life w unsilencable alerts. Anyone w less than 10 or 20 years OK, but my brain has extra backup pathways and I'm still functionable down to 50 (very rare), and I can recognize dimished coordination and the emotional shifts that accompany dips. Plus I have life experience to know my low time of day and to watch w exertion.

Oh there will be a subscription, don’t worry about that.

Having talked with T1 diabetics about such systems, the issue is that you don’t want to deplete glycogen stores just for basal management. Because your life might depend on their levels in case of a severe hypo.

> Freestyle is good enough already

In my experience, the quality control isn't very good (some patches will read much more accurately than others) and accuracy isn't that good when you get out of normal ranges.

I don't think the "invasive" nature of the Freestyle is a problem at all, but it would be nice to see some innovation on either the cost or the accuracy or both.

For closed loop better cgms will help.

Main points are (fsl2 based):

- latency, currently 10min.

- accuracy, fine in normal range, but when you have a low blood sugar suddenly the latency spikes a lot.

- values when being under the shower too high.

- start up time of 60min could be lower.

-open up the hardware for any app to read

I believe this is a reference to the tech you are talking about. I have a similar take as you: current cgm tech plus closed loop is pretty good. Self activating insulin is the first promising tech I’ve seen in the 40+ years of following the research.

https://youtu.be/lVTS_J7Xmxs?si=vemMfo3IbfMlYrUi

Low sugar awareness is a learned and developed skill. As blood sugar drops, systems shut down. The issue is that the Frontal Lobe turns off at some point. Thankfully the energy conservation reduces that impact, but once severely low I can answer a myriad of questions wrongly. I reccomend cake-mate frosting tubes used to write on cakes as an emergency prep. That can be squeezed carefully into mouth between teeth and gums and begins a bootstrap process. Another suggestion make the low fun emotionally so please try and hide anger and fear. With the sensor and fairly tight control we haven't needed the cakemate in years now but it's good to have. A Glucagon shot is another thing to keep around too. I use olive oil on my food to have unsaturated fat as a backup nonglycemic energy source. Just be mindful virgin olive oil isn't to be used for cooking, only refined non-virgin is for cooking. Also fyi Walmart has always had affordable diabetic supplies. I use their meter and strips as an extra spare. Their Relion glucose pills are very good too, I keep them everywhere and carry them with me. To equate blood sugar to body mass divide weight in pounds by 4.4 (kilograms by 2), that gives a number for what 100 equals in grams of carbohydrates to better fine tune sliding scale insulin dosages and not overcorrecting lows. Best wishes as it is very stressful for loved ones.

CGMs themselves are still inaccurate compared to needles. And even needle based meters have a lot of different levels of accuracy. A CGM, even once calibrated, may be off by as much as 10%.

Can you say more about the plastic left behind? Polyurethane coating on the cannula?

It's not approved yet anyway. But if you are ever in Berlin, let me know and I'll give you a tour.

Because the engineers of the University of Waterloo haven't shown any clinical data yet.

Thanks for calling this out. That was also my impression after having skimmed their paper: the only link to glucose monitoring is that the authors mention a few papers on the topic to motivate their research. And looking at the papers they cite, I see little evidence that this approach could work in practice in the near future. Most of the citations [2, 15, 16] are to their own work, which did not look at glucose monitoring in the human body.

This is not my field of expertise, and maybe I am misunderstanding the papers. But it seems that there is little evidence that non-invasive glucose monitoring via measuring dielectric properties works reliably in practice. No in-the-wild studies, no investigation of potentially confounding factors.

Take for example citation 22 from the paper. A study where the authors propose a new antenna design. They seem to measure how the pancreas changes size during insulin production by monitoring its dielectric properties. IIUC, they look for a dip in the frequency spectrum caused by absorption of a certain frequency band.

But their measurements show an even larger effect when measuring on the thumb instead of the pancreas. This effect is not explained at all. (My guess: after having patients fast for 8-10 hours, giving them glucose will have an effect on the whole metabolism, resulting in higher blood flow, and that's what they measured).

Also, while they operate the antenna in the GHz range, they use a cheap USB soundcard (sampling rate 44.1 kHz) for capturing the signal. I did not understand this at all. They also repeatedly use the term "dielectric radiation". Seems to be a rather uncommon term?

The "machine learning algorithms" mentioned in the title seem to be a simple linear regression? They claim an accuracy of ~90% and show some sample results. The complete study data is only available upon request, however.

[22] S.J. Jebasingh Kirubakaran, M. Anto Bennet, N.R. Shanker, Non-Invasive antenna sensor based continuous glucose monitoring using pancreas dielectric radiation signal energy levels and machine learning algorithms, Biomedical Signal Processing and Control, Volume 85, 2023, 105072, https://doi.org/10.1016/j.bspc.2023.105072

> they didn't do any testing, at all, with actual humans (or animals)

They did mention in the article that clinical trials are on-going.

I thought apple was trying to get glucose monitoring approved

https://en.wikipedia.org/wiki/Noninvasive_glucose_monitor#Ne...

(different technique)

I think this is a really important point. Making it a feature in millions of watches will drive the cost of implementation down

Why would I as a nondiabetic buy it? Out of general curiosity for my blood sugar levels?

Where can i buy?

At this state, they are likely able to do this. Not small enough for your watch but a working stationary device that can RELIABLY monitor your blood sugar. And with blood sugar I don't mean the skin prick test. If people claim they compared to the skin prick test and "it works" they have no idea what they are talking about. You have to compare venereal blood glucose with measured blood glucose and use the Clarke error grid to show what you are doing.

And yes, skin color or skin temperature (fever!) does not matter for his measurement. I once submitted an SBIR grant for this project, but have no involvement anymore with this project. The NIH found this approach "highly innovative" but thought it can not be realized bc the technology is "prohibitive expensive". I assume they did not really read the proposal. I explicitly wrote that, while such a device costs 50k on the market, a slimmed down version produced in quantities would cost a few hundred dollars. They just had a short look at the approach, googled the hardware behind it, and rejected it. An idiot and google is a very dangerous combination!

I didn't look at the competition. I knew the Dexcom brand because my friends have had good experiences with the G series, so the Stelo was an easy impulse purchase. I'm neither diabetic nor prediabetic, so I'm not a good representative of those audiences.

For someone glucose-curious like me, the Stelo is just right. The app provides a slightly laggy real-time graph of measurements (collected every 5 minutes and reported every 15 minutes). It uses some heuristics to identify rising/falling events, and it'll notify you for the steeper cases (but I'm not sure about hypoglycemic event notifications, as I had a couple of those while sleeping and found out only when I woke up). On Android, it uses the Health Connect hub to sync some health data with other apps. It provides a rudimentary event-log function to add meal, exercise, and FYI notations. You'll also get a daily time-in-range wrap-up.

The more interesting analytics are in clarity.dexcom.com, which is a website that visualizes data that the app is constantly pushing, with a couple-hour lag. There you will find more graphs, tables that group measurements by day and hour, and various expected calculations (average, standard deviation, CV, calculated GMI). And you'll also find the all-important export to CSV button, which gives you all the sensor data. Using that I was able to import everything to Google Sheets, where I did a linear regression with finger-prick measurements to ascertain the sensor's (mild) deviation.

The Stelo feature set is clearly designed to provide all the data eventually, but not quickly enough to be useful for diabetics who need real-time info for glucose management. That's how they'll continue segmenting the Stelo and G7 audiences. I have no problem with that; if OTC GCM cost continues to drop, and they become as prevalent as annual lipid/metabolic labs, I could see the incidence of lifestyle-attributable T2D dropping, which would obviously have massive benefits to society.

I've never seen ML been called AI until the past few years. And this is most likely not even ML but just fancy signal processing.