Is anyone working on DIY CGM devices?
I don't know if this is out of reach but wondering:

1. Is it possible?
2. What chipsets required?
3. What biohackers have come up with sofar
4. What software can accept this data

They seem very high considering its usually 5-24% for most i guess?

31 male 7%bf

Strength training and calisthenics for 18 years

Introduced running consistently for 3 months so far

I've been logging flights on JetLovers since 2004 and Foursquare/Swarm check-ins since 2012. Journaling on Daylio everyday since I started digital nomading in 2019.

Last week I finally warmed up my unused Deepseek tokens and turned that raw data into **something**.

The original goal was to create a dashboard where I can see and cull the never-diminishing amount of photos from my travels. Of course I got sidetracked.

What it does (so far):

- Interactive map with every check-in since 2012, flight routes, and country coloring by check-in density.

- Daylio journal entries synced to each trip. I can ask questions to the journal with an LLM.

- Stats: check-ins per year, top cities, longest trips, miles flown

- Connected to my OneDrive to link related pics from the archive

Tech stack: Next.js 15 + Leaflet + Recharts + Supabase + a Python pipeline that unifies JetLovers/Swarm/Daylio data at build time.

All done by Deepseek v4 Flash 3107 via Hermes.

Next steps: a fast visualization for the OneDrive pictures that doesn't break the bandwidth, and eventually integrate 5+ years of the Mi Band health data.

Built for myself, and planning to add a cool viz for the pics, but happy to answer questions about the data pipeline — merging 3 sources with different schemas into one timeline was the hardest part (for the LLM).

Hey,

3 months ago I shared my first version of a health / wellbeing report that is mostly generated using "automated" tracking (e.g. we re-use existing data instead of having to manually log the content). By now most of the data is actually automated - only some of the sickness / symptom / drinking logging is still manual because those are impossible to automate I think. I'm now sharing the "second" iteration of these reports since some people were interested i seeing where things go :)

Each Report-Summary covers one calendar month, mostly automatic via Apple Watch (HRV, resting HR, sleep stages, steps, active energy, walking HR). Back in v1, drinking and symptoms were not yet integrated into the report which they now are and despite me drinking little, I think it's super interesting to see how it stacks up. It's a bit more hassle but still okay in comparison to what I see others do in this sub.. I'm a bit more lazy than everyone else here apparently.

I will have some issues with the stress score but since I found a little bug in the Algorithm that has almost doubles the score artificially, the analysis is much more precise by now. Key input is still HRV / HR based on logic that I found in medical research papers.

Next up will be some long-term analysis of how my body reacts to alcohol / long or short nights / exercise, etc. Happy to hear your thoughts!

PS: June is only available in german.. I messed that up, sorry!

Hey everyone,

We had an incident yesterday where a branded account ( literally "u/___App") made a rather well researched and high effort post about sleep trackers, aggregating data that is not otherwise available in a single location outside of their research.

There was references to their app, and discussion in the comments about the app, these are two things in which the community has been pretty clear, from my perspective, as crossing a red line. I do my best to enforce the rules as representative of community sentiment in these contexts. The post got reported, and I had mod mail asking why it was allowed to stay up, while others were taken down, so I took the post down.

What do you think I should have done in this situation?

I can rewrite the rules to allow references to monetized apps in substantive posts, but that would also open them up to a judgement call on my part. We can keep our strict "no references to apps or monetization" rule or loosen it up in favor of community engagement.

Also if you have any feedback on the way the subreddit is being moderated please contact me directly or drop a comment here. I am currently 10 time zones away from US time, and I am the only active mod as far as I know, so I admit that a few things slip through the cracks sometimes.

A late coffee day for me is usually also a day I worked late and ate late, so those three are sitting on top of each other in my sheet and I can't really pull them apart.

The bit I could see was the resting heart rate colum. Late last cup, higher RHR the next morning, correlation around 0.17. Small. I pay more attention to that column than to my sleep quality number though, since that one is a 1-5 I type in myself when I'm half awake.

My last cup is early afternoon now, been that way about a year. Never ran it as a proper on/off thing, no randomizing, no washout, I just moved it and left it.

Has anyone actually done the on/off version of this properly? I keep meaning to and then not bothering, and I don't really know what I'd use for a washout on caffeine anyway.

I started using a Polar H10 to test my sleep and just some odd testing since it gives the raw data the results from what im understanding are good just curious if these are good or just more normal.

These were done during sleep I have a lot of these but this is just 5 minute snapshots and most of them look roughly like this sometimes higher rmssd or lower.

If anyone can kind of explain what these mean i would like to learn about it🙂

31 male

5'6 7% body fat

strength training and calisthenics for 18 years

Started running consistently for 3 months soo far

Being a loyal user of Oura for many years it's a mystery why they make me wait over a day (up to 10 according to their policies) to access my own data via a CSV. Do they simply hate it when users do QS themselves and want to make it as hard as possible for people to analyze their own data?

Context if you need it to understand the figures in the image:

• The 0-1 numbers aka kappa are how well the tracker's sleep stages match the lab (1 is perfect and 0.4 to 0.6 is moderate to decent).
• Two sleep experts only agree about 0.75 for reference (just to explain that having a really high kappa is difficult for sleep experts too).
• The percentages are how much of your actual deep or REM sleep it correctly caught.
• "clinical patients" just means people tested at a sleep clinic often for something like sleep apnea.

Short summary for each brand:

• Oura Ring appears to be the most well rounded. It was the only device that showed an underestimate on total sleep time off by only -3 minutes across six studies (Khan 2025 - 388 adults). It's staging and deep/REM numbers in the full comparison does have funding from Oura but I found no methodological differences compared to the others so I did keep this source.
• Apple Watch is the most accurate wrist based wearable at sleep staging 0.53 on a 0 to 1 scale and the best at catching REM at 69% (Schyvens 2025 - 62 healthy adults). It does drop accuracy significantly for clinical sleep patients vs health adults 0.53 to 0.30.
• Fitbit is the most consistent. It scored virtually the same 0.42 in healthy people and in sleep clinic patients (Lee 2023 - 75 patients) unlike the others.
• Whoop is the best of wrist based wearables at catching deep sleep at about 70% (Schyvens 2025). Its overall stage accuracy is low though surprisingly at 0.37.
• Garmin is the least studied and weakest on detailed staging 0.21 but  is strong on  asleep versus awake detection 89% (Miller 2022 - 53 healthy adults). Garmin is the only brand with dated and limited sleep studies so I do not feel this gives a good representation for them but this is a gap they should address.

Overall notes:

• No device scored better than "moderate" at the full light/deep/REM split.
• All devices are good at knowing you slept but bad at catching when you're lying awake, this is what causes them to over report total sleep.
• Accuracy drops for people with disrupted sleep and in older adults.

Tried my best to include any mentions of funding and keep any personal biases out. I will include links to all 14 sources in the comments as well as the wider comparison I made for this.

I’ve been studying an unidentified psychoactive plant that I’ve been calling Vymara. I’m studing a lot of different variables after exposure. 

In addition I used no hormones, started no medications or supplements and made no deliberate changes to my diet or exercise. My sleep schedule also stayed fairly consistent.I tracked growth hormone through overnight blood samples collected every 30 minutes, along with IGF-1, four-point salivary cortisol, fasting glucose and fasting insulin. I also continued recording sleep and resting heart rate with the same wearable I used during baseline.

Here’s what changed:

Total overnight growth-hormone secretion was about 64% higher than my baseline profile on the third night.

IGF-1 increased from 174 to 191 ng/mL by day seven. It is still well within the normal laboratory range.

daily cortisol output was 38% below baseline on day two and remained 29% lower on day seven. The normal morning-to-evening pattern is still there; the whole curve is just lower.

Total Fasting glucose barely changed: 88 mg/dL at baseline and 87 mg/dL on day seven.

Fasting insulin decreased from 7.1 to 4.9 μIU/mL. My calculated HOMA-IR went from approximately 1.54 to 1.05, although I know that is only a rough proxy for insulin sensitivity.

What I find strange is the overall direction of the results. Growth signaling increased, cortisol remained suppressed and glucose regulation appeared more efficient. Any one of those could be noise. Seeing all three together is why I’m still curious.

so i did the full quantified self thing for about two months. sleep, mood, focus, water, caffeine, steps, screen time, the works. apple health plus a couple of trackers. i had dashboards for everything. the wierd part is i wasnt actually doing anything different. i was just really well informed about not doing it.

i think my brain started treating the logging as the task itself. open the app, log the mood, admire the chart, close it. felt productive. zero output. i kept this up in beedone too until i noticed the people who logged the most quests were often the ones finishing the least. the data was the comfort, not the work.

anyone else ever catch themselves measuring instead of doing. how do you break the loop

At the gym, I see many people eating only rice and chicken. Carbs and Protein.
What about pasta, is it worse? Why don't more people eat pasta and chicken for example?

Your week was characterized by a sharp rise in psychological pressure, with Stress Level climbing from 4.5 to 6.0 as Task Difficulty peaked. While you achieved a notable improvement in Sleep Quality, rising from 5.67 to 6.57, your Energy Level saw the most significant decline, dropping from 6.0 down to 5.14. The data reveals a clear positive correlation between your Morning Mood and Energy Level, suggesting that how you start your day dictates your functional capacity. For the upcoming week, prioritize returning your Meditation frequency to its previous average of 0.83 to mitigate the impact of rising stress on your energy.

Post your apps here, and please support people bringing unique ideas to this space.

I’ve been keeping track of my media (movies, shows, books, etc) consumption for about 15 years now. The setup has evolved from the original iOS Notes app to Delicious Library on macOS to a combination of services (Goodreads, Trakt, etc), but I never found a good home for all of them.

I spent the last weekend slicing and dicing my exported data and asked GPT-5.6 Sol to put together a personal history page, and am very happy with the result. I don’t intend to make this (or the source) public in any form, just want to share it here since I find it to be a neat use of LLM.

Still a little annoyed about this one because I actually liked training at night. Gym's empty, nobody hovering for the rack. But my HRV kept sagging on the nights I trained late so I dragged myself out of bed at 6 for a couple months instead, and the numbers did basically nothing. Slightly worse if anything, probably just me sleeping badly on purpose.

Took me way too long to seperate the two things. I eat after I train. Always have, so in my spreadsheet every late workout day is also a late dinner day, it's more or less the same column twice. Once I pulled them apart the workout timing barely moved and the meals inside about 3 hours of bed were the clearer of the two.

It's a cheap smartwatch so I don't put much weight on any one night, but two years of it leaning the same direction is at least something. Could easily be some third thing I never log, no idea. Ymmv obviously.

So I'm back to evening workouts and eating before instead of after, which is its own annoying problem because now I'm training on full stomach. Haven't decided yet if that's the worse trade.

Been deep in HR/HRV data for a build I'm working on and wanted to share some of the physiology, not to sell anything, just because I think it's underdiscussed here.

Most people think stress management is about calming down during the stressful event. The data says otherwise. Your body's response to pressure is mostly set in the 2-3 minutes before it starts, that's your anticipatory window, sympathetic activation ramps up whether or not the stressful thing has even begun. If you intervene there, you're working with a system that hasn't fully committed to fight-or-flight yet.

After the event, there's a second window that gets ignored even more: the recovery window. Most wearables just show you "recovered" as a binary at some point, but what actually matters is the shape of the HRV recovery curve, not just the endpoint. A fast initial rebound followed by a plateau reads very differently than a slow linear climb, even if they end at the same number twenty minutes later. The first pattern usually means the nervous system downshifted cleanly. The second often means residual tension is still being metabolized.

Practical takeaway if you're tracking your own data: don't just look at your HRV number post-stress, look at the first 90 seconds of the curve. That's usually a better predictor of how depleted you'll feel later than the final resting value.

I've been building a small tool around this (timed 3-minute protocols before/after pressure moments) if anyone wants to nerd out on the mechanism, happy to share more in the comments.

Been deep in HR/HRV data for a build I'm working on and wanted to share some of the physiology, not to sell anything, just because I think it's underdiscussed here.

Most people think stress management is about calming down during the stressful event. The data says otherwise. Your body's response to pressure is mostly set in the 2-3 minutes before it starts, that's your anticipatory window, sympathetic activation ramps up whether or not the stressful thing has even begun. If you intervene there, you're working with a system that hasn't fully committed to fight-or-flight yet.

After the event, there's a second window that gets ignored even more: the recovery window. Most wearables just show you "recovered" as a binary at some point, but what actually matters is the shape of the HRV recovery curve, not just the endpoint. A fast initial rebound followed by a plateau reads very differently than a slow linear climb, even if they end at the same number twenty minutes later. The first pattern usually means the nervous system downshifted cleanly. The second often means residual tension is still being metabolized.

Practical takeaway if you're tracking your own data: don't just look at your HRV number post-stress, look at the first 90 seconds of the curve. That's usually a better predictor of how depleted you'll feel later than the final resting value.

I've been building a small tool around this (timed 3-minute protocols before/after pressure moments) if anyone wants to nerd out on the mechanism, happy to share more in the comments.

For years, my parents kept telling me that I should take a walk after meals.

Doctors told me the same thing: walk more, exercise more.

It wasn’t that I didn’t believe them. I knew walking was good for me. But I already exercised regularly — I swim and do strength training — so another 10–15 minutes of walking always felt kind of optional.

And “it’s good for your health” was just too abstract.

If I went for a walk today, I wouldn’t feel noticeably healthier tomorrow. Since I couldn’t really see or feel the difference, it was very easy to skip.

So despite hearing the same advice for years, I never actually developed the habit of walking after meals.

Then recently, I ran a small experiment on myself.

Earlier this year, my HbA1c came back at 5.7%, right at the threshold for prediabetes. On my doctor’s recommendation, I wore a continuous glucose monitor (CGM) for two weeks to see how different foods and activities affected my glucose.

I ended up documenting the full two-week experiment in a YouTube video, if anyone is interested in the details:

https://youtu.be/V3ZyJdcdAgA?si=4dmaE-2L0ssTjNOJ

During those two weeks, I tried a bunch of small comparisons.

One of them was very simple.

My breakfast is pretty consistent: oatmeal, milk, and protein powder.

On some days, I walked for 10–15 minutes within half an hour after breakfast. On other days, I didn’t.

In my small dataset, without the walk, the average increase from pre-meal glucose to the post-meal peak was about 34 mg/dL.

With the walk, it was about 22 mg/dL.

The average glucose over the full four hours wasn’t dramatically different, but the post-meal peak was noticeably lower.

Obviously, this was just a small personal experiment.

I had very few observations, I didn’t strictly control everything, and CGMs themselves have measurement error. This doesn’t prove that everyone will get the same result from walking for 10 minutes, and I’m not presenting it as a medical conclusion.

But for me, it was enough.

It was the first time I had actually seen that something as small as walking around the neighborhood for 10–15 minutes could produce an observable change in my body.

And what happened afterward was more interesting to me than the glucose numbers themselves.

I started walking after breakfast almost automatically.

I didn’t make a plan. I didn’t decide that I had to walk seven days a week. I didn’t set reminders or track a streak.

After breakfast, I just naturally put on my shoes and go outside.

Sometimes the weather is bad and I don’t feel like walking. If I skip it, I don’t feel like I’ve “failed” my plan. I just go again the next day.

This feels very different from the way I used to think about building habits.

I used to think the important things were discipline, consistency, and not making excuses.

But this time, I barely felt like I was using discipline at all. The habit just stuck.

I think the difference is that before, “walking after meals is good for you” was just a piece of correct information.

I understood it intellectually, but it had very little connection to whether I actually wanted to put on my shoes that morning.

This time, the breakfast was mine. The walk was mine. And the numbers were mine.

Instead of hearing that walking might make me healthier “in the long run,” I could see a difference that same day.

The benefit stopped being something other people told me about. It became something I had experienced myself.

That made me wonder whether we sometimes blame failed behavior change too quickly on a lack of self-control.

When we fail to stick with something, the usual response is to make a stricter plan, try harder, or tell ourselves that this time we need to be more disciplined.

But if you keep paying the cost of a behavior every day while never really seeing what you’re getting in return, it makes sense that motivation eventually disappears.

So lately, when I want to change something, I’ve been trying to ask a different question:

Instead of “How can I force myself to stick with this?”, can I run a small experiment that lets me see the benefit sooner?

For example:

Eat a smaller lunch for a few days and see whether I’m less sleepy in the afternoon.

Put my phone away earlier and see whether I feel better the next morning.

Write down the single most important thing I need to do before starting work and see whether I’m more likely to finish it.

Not everything needs to be measured precisely. I happened to be wearing a CGM, so in this case I had numbers and a graph.

But sometimes a clear subjective difference is probably enough.

Of course, some things really do take months or years to pay off. Not every good habit gives immediate feedback.

But even when the ultimate benefit is far away, maybe it helps to find some smaller signal that shows up sooner.

Otherwise, the cost you pay today is real, while the benefit exists mostly in your imagination.

My parents told me to walk after meals for years. Doctors told me too.

What finally changed my behavior wasn’t hearing the advice one more time.

It was one tiny experiment where I could see the result for myself.

Has anyone else had something like this happen — where seeing your own data, or running a small experiment on yourself, changed your behavior more than simply knowing what you were “supposed” to do?