r/QuantifiedSelf

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. So I wrote 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.

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.

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?

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

Here's the quick version 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.

For most of my life I tried to be someone else. I'd find someone I admired, decide they were better than me, and copy them. That mindset pushed me into a business I never enjoyed and only started because I looked up to one specific guy. It failed. I felt completely lost.

Around that time I was obsessively tracking my sleep with a Whoop, trying to optimize it. I kept getting good recovery scores. And I was still exhausted, yawning through entire afternoons, dead by 2pm. That's when it clicked: the score doesn't do anything. It just confirms you slept well or badly. Cool. Now what? Knowing isn't fixing.

So I built the thing I actually wanted. It takes the data your wearable already collects sleep, recovery, heart rate, and turns it into a daily protocol instead of another number. It tells you what supplements to take based on your metrics, predicts your most productive hours and gives you the exact time window when you should do deep focus tasks and light focus tasks, it tells you how much caffeine you have in your system left based on your first coffee taken and notifies you when you should take the next caffeinated drink for maximum productivity, it even tells you when to nap so your energy lasts the whole day instead of crashing and much more...

It's on the App Store as RizeAI https://apps.apple.com/us/app/rizeai-maximize-your-energy/id6762402079. i built by myself, it's early stage right now, and I want honest feedback, what's confusing, what's missing, what you'd never use. Tear it apart.

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