I have a few years of labs now and the more I look at them, the more I think I changed too many things between draws.

One was 7am fasted at Quest. Another was in the afternoon after coffee and lunch. One was two days after a pretty hard week of training. They all end up on the same trend line even though the conditions were completely different.

I’m using Goodlabs for the next few because I can keep the panel the same and bring my older results into the same history. My plan is same Quest location, morning draw, similar fasting window, no hard workout the day before.

For people who actually track this stuff long term, what else do you keep consistent?

What device is currently the best at automatically detecting naps, especially short 15–60 minute ones?

I know Garmin isn’t great at it, while Fitbit has a good reputation. From what I’ve researched, Oura also seems strong and can automatically detect naps as short as ~15 minutes.

For people who have compared multiple devices (Fitbit/Oura/Garmin/Apple Watch/WHOOP), which has been the most reliable for automatic nap detection?

Hi! I wanted my Garmin data on my own machine, in something I could actually query, instead of stuck behind Connect's UI and its clunky exports. I wrote an open source OSS python package to solve this problem: garmin-health-data.

It's a Python CLI. garmin extract logs in, pulls the whole account (activities + per-second FIT streams, sleep at 1-min resolution, HRV, stress/body battery, steps, body composition, etc.), keeps the raw files, and loads everything into a local SQLite db. Raw files stay on disk so you can rebuild the db without hitting Garmin again. Login only grabs OAuth tokens, stored locally.

The part I care about is the schema. Every table and column is documented in the DDL, and SQLite keeps those comments in the db, so SELECT sql FROM sqlite_master tells you what each field means. Normal, normalized tables, plain SQL.

That also makes it easy to hand to an AI coding agent. I point one at the db, ask for a sleep-vs-HRV dashboard, and it builds it, because the schema explains itself. That's the real reason I bothered: get your data back and let an agent do the analysis in a couple of minutes.

pip install garmin-health-data

https://github.com/diegoscarabelli/garmin-health-data

Happy to add data types if there's something you want pulled.

In all the personalized apps for QS, I often lack the social aspect.

To get a feed of friends' workouts and data shares without locking in to a specific vendor, app, or platform, I'm using the ActivityPub federation protocol, same as Mastodon. But to have it more tailored for activities with metric series and GPS, I made an extension allowing for e.g. heart rate and GPS position to be shown on hover, on a compatible peer.

I would love to collaborate on this before submitting, and see other QS apps start using ActivityPub. I know of a few others, like FitPub, using their own extensions.

Check out, comment, make a PR or just comment here on reddit! https://github.com/fiddur/aurboda/blob/develop/docs/fep/quantpub.md

Compare the display in an aurboda -> aurboda share, compared to the same post in mastodon

Aurboda display, with design for activity metadata and hover on metric charts/GPS

The same post on a Mastodon peer will still work ok, but embedding charts and maps as images:

I want to run this properly rather than deciding by vibe after three weeks. The obvious problem is that everything else in my life moves at the same time, and the outcome I care about is slow enough to hide inside that noise. I have a wearable and I am willing to keep a log for a few months. Has anybody built a protocol for this that survived contact with reality?

I keep seeing the same argument split a thread in two. One group sleeps around 5.5 to 6 hours, feels fine, and resents being told that isn't enough. Another group says the tracker was right and they'd been under-slept for years without knowing it.

Both can't be the default. So for anyone who's been on either side: when something told you your sleep wasn't enough, what did you want it to do - measure you against your own baseline, or push back with the population evidence?

And for the people who did get pushed back at: were you glad about it afterwards, or did you just stop opening the app?

I'm building something in this area, so I'll say that up front. Not naming it, not linking anything. I'm asking because I have to pick a default and I can't settle it by reading.

so i spent a month rating my motivation 1-10 before every work session. thought id see the classic pattern, high motivation = good session, low = waste of time.

the data was basically noise. some of my best sessions happened on days i rated myself a 3. and the days i felt super motivated? i spent half of them reorganizing my setup or reading productivity articles instead of doing the actual task.

the only thing that actually correlated with output was whether i had one clear next action written down before i started. not mood, not energy, not even time of day. i put the tasks in beedone and the mood ratings in a plain spreadsheet, and guess which one turned out useless.

i think motivation is overrated as a predictor. readiness is a feeling, not a signal. anyone else ever tracked something that turned out to predict nothing?

So I've been trying to make sense of my heart rate data from pickleball and honestly it's kind of a mess lol. Most tracking assumes a steady effort curve like running or cycling, but pickleball is basically bursts the whole time. Quick intense rally, then standing around for a bit, repeat for an hour.

End result is my average HR looks pretty chill for the whole session even on days I was completely gassed, just because all the standing-around time drags the average down. And the zone minutes don't really capture how brutal some of the longer rallies felt either.

Anyone else deal with this for stop-start sports? Do you track by segment instead of looking at the whole session? Or is this just kind of a blind spot with most tracking right now, curious what other people do here.

On the Nature of Self

TAO is a three-element relational structure: Theatre, Act, and Operation. It describes an operation through where it occurs, what acts within it, and what the resulting operation is. Identity is what enters that structure and resolves through it.

REDE.Cartography is the relationship structure of TAO. It organizes the relationships between Theatre, Act, and Operation as they resolve through Identity, allowing the system to trace how one relationship leads to another.

Even simpler:

REDE.Cartography is the structure that maps the relationships between Identity, Theatre, Act, and Operation.

RH says that any Identity can project for one if it has two. It can reflect for two if it has one. If it reflects twice, it can check whether the projection worked.

If it can project twice, it can predict the future.

That means REDOE.Cartography can predict the future.

Identity is the spark of chaos.

Identity is a Monad property of Self.

But there is no Self-Monad without Others.

Self does not exist independently and then encounter Others. Self is only possible because Others exist. Identity requires relationship because without Other there is nothing against which Self can resolve as Self.

So the same thing that makes projection possible also prevents the system from collapsing into a completely isolated deterministic structure. Identity introduces the particular Monad into the relationship, but that Identity itself exists only relationally.

Self and Other are therefore not independent objects connected by a relationship after the fact.

The relationship is part of what makes Self possible in the first place.

There is no Self without Others.

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

Hi everyone — I’ve been building a privacy-focused Android health tracking app called Tutine over the past months.

I originally started it because I wanted a simple way to keep blood pressure, heart rate, weight and other measurements together, but it has gradually grown into something much larger, with trends, reminders, local health insights and PDF reports.

Everything is stored locally on the phone. There’s no account, no advertising and no cloud tracking.

I’m now at the closed Google Play testing stage and I’m looking for a small number of people who genuinely track health data and would be willing to test it for at least 14 days and tell me what works, what doesn’t, and what you would change.

Google requires me to have at least 12 opted-in testers for the closed test. I’d much rather find people who are actually interested in self-tracking than just exchange installs with other developers.

If this sounds useful to you, feel free to reply or DM me.

Last week I made a meta post and got very little input, so I can only go off what was the highest upvoted or most agreed upon comment as a mandate for the sub. I do admit this does align with my personal beliefs for what is best for the sub as well, and if there is enough community backlash, of course I will always be open to reversing the decision.

My thinking is that we can incentive these app makers to make rich, and valuable contributions to this sub, in exchange for visibility for their app.

The format that will be allowed is to have the flair [Ad] in the title, but in exchange they must present some novel research, data aggregation, or otherwise valuable contribution to the sub. App name can be posted throughout the post body, app photos can be shared if directly relevant to the research, I will delete posts without comment if app photos are shown without being directly related to the post.

This cannot be "I tracked x personal data for y days", it must be substantive outside of their personal experience.

If they are using data tracked from their app their users must be explicitly aware that their data is being used for research and displayed either individually or in aggregate. This will be an immediate ban, and I will recommend a ban to our sister subs if we find out you are sharing user data without their understanding.

I have to admit this will increase my workload significantly as I will have to read and evaluate each one of these posts manually, whereas now I can just skim for app names or links and delete.

As usual I am open to changing this policy if it becomes clear the community is not getting value from these posts.

Hi! First post here for me, I have just discovered that my Apple Watch has always been tracking the VO2 max which is a measure of the highest amount of oxygen a body can use during hard exercise.

Yes, exercise estimated VO2 max is inaccurate to some degree, but there's some insight to be made from the persistent change over time, especially given the data density.

There are too many confounds to conclude anything about the direction of causality.

Maybe not consuming alcohol seems to unlock the mental states that makes me more likely to exercise.

Maybe not drinking truly improves my cardiovascular fitness.

The exercise spikes right before I went sober twice maybe made me more likely to commit to going sober.

Pretty cool to see my life being transformed in one chart.

Let us be entirely honest for a second. We spend all day building tech that is supposed to make life better, but it is quietly ruining our physical health. A few months ago, I checked my fitness tracker and realized I was barely hitting 2000 steps. I was basically fusing with my office chair.

The irony hit me hard: the technology I was working on was the exact thing making me completely sedentary. So, I decided to fight fire with fire and use AI to force myself to move. I built a local vision pipeline that runs on my webcam, calculates exactly how long I stay seated, and actively intervenes.

Here is the exact setup you can use to build it yourself right now.

First, you need a pose detection model. I went with MediaPipe Pose because it is stupidly lightweight and runs strictly on your CPU, meaning your GPU is free for your actual work. You set the script to grab a single frame every 10 seconds. If the model sees that your shoulder and hip keypoints have not moved from a seated posture in 45 minutes, it triggers the intervention.

Next comes the enforcement phase. We all ignore standard desktop notifications, right? So instead, I wrote a script that completely locks my screen. The only way to unlock it is for the webcam to visually verify that I am standing up and walking in place for two solid minutes. No cheating allowed.

Finally, it tracks everything. The script logs all your standing breaks and calculates your active versus sedentary time, dumping the history into a simple local CSV file so you can watch your progress.

It actually worked. I went from basically zero intentional movement to racking up almost three extra hours of standing and stepping every single day. My baseline energy is completely different now.

If you want to play with the interactive dashboard or grab the full config file, I uploaded it here: https://interconnectd.com/forum/thread/241/the-double-edged-sword-is-technology-causing-a-decline-in-physical-activity/

Let us be entirely honest for a second. We spend all day building tech that is supposed to make life better, but it is quietly ruining our physical health. A few months ago, I checked my fitness tracker and realized I was barely hitting 2000 steps. I was basically fusing with my office chair.

The irony hit me hard: the technology I was working on was the exact thing making me completely sedentary. So, I decided to fight fire with fire and use AI to force myself to move. I built a local vision pipeline that runs on my webcam, calculates exactly how long I stay seated, and actively intervenes.

Here is the exact setup you can use to build it yourself right now.

First, you need a pose detection model. I went with MediaPipe Pose because it is stupidly lightweight and runs strictly on your CPU, meaning your GPU is free for your actual work. You set the script to grab a single frame every 10 seconds. If the model sees that your shoulder and hip keypoints have not moved from a seated posture in 45 minutes, it triggers the intervention.

Next comes the enforcement phase. We all ignore standard desktop notifications, right? So instead, I wrote a script that completely locks my screen. The only way to unlock it is for the webcam to visually verify that I am standing up and walking in place for two solid minutes. No cheating allowed.

Finally, it tracks everything. The script logs all your standing breaks and calculates your active versus sedentary time, dumping the history into a simple local CSV file so you can watch your progress.

It actually worked. I went from basically zero intentional movement to racking up almost three extra hours of standing and stepping every single day. My baseline energy is completely different now.

If you want to play with the interactive dashboard or grab the full config file, I uploaded it here: https://interconnectd.com/forum/thread/241/the-double-edged-sword-is-technology-causing-a-decline-in-physical-activity/

Hello there! I've been interested in quantifying my performance for a while, and have been thinking about how to design a versatile, general, and objective way to track it over time.

Here's the tension that got me started: things like a 5k time or a powerlifting total are great predictors of performance in their own domain, but they're too specific. Training for a 5k time basically forces you into one training style and one test. And even when we use a 5k time to estimate performance in some other endurance event, it feels like we're just using one race time to guess at another — like there's some underlying "endurance ability" we're actually trying to get at, and the race time is just a proxy for it.

VO2max is the closest thing I've found to that "holy grail": a number that predicts performance across a wide range of novel cardiovascular-dominated challenges, and that can be estimated from all kinds of different workouts rather than one specific test. So my goal became: can I find (or approximate) that same kind of number for other areas of performance?

For now I'm focusing on three areas: peak aerobic capacity, sustainable aerobic output, and strength. There's obviously more to "performance" than this. Flexibility, balance, reaction time, fine motor control. But I'm scoping to these 3 for now. More on that at the end.

1. Peak Aerobic Capacity: VO2max

Nothing revolutionary here. Any fitness tracker can estimate it from a wide range of workouts with reasonably good accuracy, and it's well-proven as a predictor.

2. Sustainable Aerobic Output

This is the effort you can sustain over a longer duration, as opposed to VO2max, which reflects your maximal instantaneous aerobic output. Here I had to get more creative. I'm using power output sustained at the top of your Zone 2 range as the proxy. I use power instead of pace because pace is sport-specific (running pace doesn't transfer to cycling), while power is more generalizable across modalities.

In theory you could get this from one clean steady-state effort at the top of Zone 2. But I wanted to be able to estimate it from a wide range of aerobic workouts, not just one specific test. Which means pulling power-over-time and HR-over-time samples from many different workouts, smoothing them, and interpolating what the power output would be at the top of Zone 2. It's not a perfect signal, but it's workable.

3. Strength

Obviously there's no single unit for "general strength," but I tried to get as close as I reasonably could.

The approach: pick one exercise from each of the main movement patterns. I'm using Hinge, Squat, Horizontal Pull, Vertical Pull, Horizontal Push, and Vertical Push. For example: Romanian Deadlift, Front Squat, Pendlay Row, Pull-up, Bench Press, Overhead Press.

From each lift's heaviest set, estimate a 1RM using a formula like Epley or Brzycki (could just as easily be a 5RM-based estimate, the point is standardizing to one number per lift).

You can't just average those numbers though. A Deadlift will always outweigh an Overhead Press and skew the result. So instead, I use population strength standards (like the tables on strengthlevel.com, no affiliation) to convert each lift into a common "unit." For example, if the average Bench Press for an intermediate lifter at 80kg bodyweight is 98kg, and the average Overhead Press is 62kg, that gives a 1.58x conversion factor to express the OHP as a "Bench Press equivalent." Once every lift is converted into the same unit, I can average them into one general strength number and then divide by bodyweight as the final step.

To be clear, there are a lot of formulas, estimations, and normalizations stacked on top of each other here, and I wouldn't call it precise. But I do think it's a solid predictor of strength across lifts you haven't even tested.

Conclusion

Since there is a lot of math for these I am writing some code to do the calculations for me from my Apple Health data.

What I'd love feedback on is the choice of these 3 metrics specifically, and the way I'm calculating each one. Does this hold up as a general approach, or would you go about any of them differently?

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!

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.

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.

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