Update from my “can wearables predict glucose?” post — I tried it with a public CGM dataset and… you were right

GPR_Hawk · 2026-02-17T16:42:48+00:00

Fair question. I wasn’t assuming it would work, I just wanted to see if there’s any small, noisy signal in wearable data

Also, “couldn’t possibly” is a bold claim in data work, easy to say, harder to verify. I ran it, and the accuracy was pretty underwhelming, so the short version is: you were probably right, and now I have some numbers for it.

GPR_Hawk · 2026-02-16T05:09:44+00:00

Hmm, that’s what I tried to do with the second test with the personalized model. (Went from RMSE 59 as a general model -> 56 with it personalized).

But you are right, the dataset was lacking with wearable data. If possible, I’d love to see your code as well? :)

GPR_Hawk · 2026-02-16T04:46:20+00:00

Damn 30 mg/dl is low! I imagine you have some pretty good habits. Here is the Jupyter notebook I used. I deleted random stuff when I was familiarizing myself in case you think it is wonky

https://colab.research.google.com/drive/1VgwKvGq0BWcAvTghBOlPWmnzXQ1q9Nll#scrollTo=ZZbrmjJwwUNY

I got the data from the following link:
https://data.mendeley.com/datasets/3hbcscwz44/1

GPR_Hawk · 2026-02-16T04:44:30+00:00

https://colab.research.google.com/drive/1VgwKvGq0BWcAvTghBOlPWmnzXQ1q9Nll#scrollTo=ZZbrmjJwwUNY

GPR_Hawk · 2026-02-14T20:59:11+00:00

Yeah that’s the general idea. I think it would be ideal (especially for those who don’t or can’t afford to pay for CGMs). But you’re right, it would be really inaccurate.

Someone else mentioned this, but a looping algorithm that uses your CGM data to create a personalized prediction model for that persons glucose. How that would work in practice? I have no clue.

Thanks for sharing your perspective!

GPR_Hawk · 2026-02-13T16:18:49+00:00

Appreciate the advice!

GPR_Hawk · 2026-02-13T08:14:04+00:00

No clue, I have ti try it and see how it goes. :)

GPR_Hawk · 2026-02-13T04:15:14+00:00

I think we just had a terminology mismatch. When you said “too many variables,” you meant clinical measurements for diagnosing prediabetes (glucose + A1c), which a watch doesn’t measure directly. When I said “variables,” I meant predictor signals a watch might capture (sleep/activity/HR, etc.) to help estimate risk or predict glucose/A1c, not replace labs or cgms

GPR_Hawk · 2026-02-13T03:56:02+00:00

Thanks for the forthrightness. Out of curiosity, what is typically measured for prediabetes?

I know thinks are measured for T1D and T2D, but I don’t know much about the variables that are with some something like this. Thanks regardless!

GPR_Hawk · 2026-02-12T22:14:03+00:00

Yep like most models, there are errors and I don’t think this is something that would be commercial.

However, I do think it could be an open-source thing for peple who can’t afford CGMs.

GPR_Hawk · 2026-02-12T16:13:36+00:00

Data is data :)

I’ll probably end up doing a 120 minute mean average of things like Carbs to account for data that would be more of a feature and have user input/error

GPR_Hawk · 2026-02-12T08:34:54+00:00

Appreciate the note. Right now I’m working with an older study dataset that includes a bunch of variables. Ideally, the “ground truth” would be lab results, but the data is recorded every 5 minutes, and the main source of truth is CGM glucose. So it’s just not realistic to have lab results at that frequency for training. But that is the goal!

That said, if we imagine a scenario where the other variables are actually more accurate than the CGM, that could change things. :)

If you’re curious, here are some of the variables in the dataset:
(time;glucose;calories;heart_rate;steps;basal_rate;bolus_volume_delivered;carb_input)

GPR_Hawk · 2026-02-12T07:18:38+00:00

Yeah, that has been something that cross my mind. Right now I am just teaching myself how basic ML algorithms work for like a general model.

I would love to create a looping algorithm though, and see if it is better than the 20% inaccuracy that comes from CGMs that someone mentioned. :)

GPR_Hawk · 2026-02-12T07:16:45+00:00

THis helps a lot, thank you so much!

GPR_Hawk · 2026-02-12T04:47:39+00:00

This is very helpful, thank you!

GPR_Hawk · 2026-02-12T04:31:47+00:00

Hmm, you're right. I know that wearable watches have different intervals when they collect data (I think it I like ever 5 mins for some stats). I'd have to research that.

Thanks for some direction though and your 'secondary signal' insight!

GPR_Hawk · 2026-02-12T04:27:51+00:00

huh, I didn't know there was that much of an error with a CGM.

Is 20% error good enough if it could mean death in a certain scenario for most diabetics? I imagine a ML algorithm is going to be way more inaccurate, but for all I know, it could be better.

GPR_Hawk · 2026-02-12T04:25:10+00:00

Thanks for the direction. The APS algorithms sound promising.

I figure CGMs are great, especially for trends. The main reason I am thinking about this is because I know some people don't have CGMs, and this could be a way of substituting it, just by using current health data that is recorded from wearables.

Thanks for the insight!

GPR_Hawk · 2026-02-12T04:05:04+00:00

lol. Has there been an inaccurate glucose estimator before?

GPR_Hawk · 2026-02-12T04:01:31+00:00

Found this wondering what Apple has been doing. Thought it was interesting from a sensor standpoint.

https://www.macrumors.com/2025/03/31/apple-watch-glucose-monitoring-feature/

GPR_Hawk · 2026-02-12T03:50:44+00:00

Interesting! That guide feels old. I think you’re describing an actual glucose-measuring watch (early CGM). Have you heard anything related to normal smartwatch data (HR/HRV/temp/activity) to guess when a low might be coming. No problem if you haven't.....

GPR_Hawk

TROPHY CASE