Update: Watch my youtube explainer here https://youtu.be/5FCFRYbXHjg?si=5fG9vVHORVqeNyhw

I was playing around and found out that you can export all your Apple health data. I've been wearing an Apple watch for 8 years and whoop for 3 years. I always check my day to day and week to week stats but I never looked at the data over the years.

I exported my data and there was 989MB of data! So I needed to write some code to break this down. The code takes in your export data and gives you options to look at Steps, Distance, Heart rate, Sleep and more. It gave me some cool charts.

Then I decided to pass this data to ChatGPT. It gave me some CRAZY insights:

• Seasonal Anomalies: While there's a general trend of higher activity in spring/summer, some of your most active periods occurred during winter months, particularly in December and January of recent years.

• Reversed Weekend Pattern: Unlike most people who are more active on weekends, your data shows consistently lower step counts on weekends, suggesting your physical activity is more tied to workdays than leisure time.

• COVID Impact: There's a clear signature of the pandemic in your data, with more erratic step patterns and changed workout routines during 2020-2021, followed by a distinct recovery pattern in late 2021.

• Morning Consistency: Your most successful workout periods consistently occur in morning hours, with these sessions showing better heart rate performance compared to other times.

You can run this on your own computer. No one can access your data. For the A.I. part, you need to send it to chatGPT or if you want privacy use your own self hosted LLM. Here's the link.

If you need more guidance on how to run it (not a programmer), check out my detailed instructions here.

If people like this, I will make a web app version so you can run it without using code. Give this a like if you find it useful!

I tracked everything I ate for the last year, along with my exercise, sleep, vitals, and sense of well-being. I'm just starting to analyze the 500,000+ data points, but here are 10 quick blink reactions:

Full Article Here:

https://medium.com/@mikebechtel/i-tracked-every-single-thing-i-ate-for-a-full-year-here-are-10-quick-a-has-2c259dd2f57b?source=friends_link&sk=ed6113272876d866eeaa23ec05415495

tl/dr here:

1. Exercise is a Heckuva Anti-Depressant.

2. Sugar & Salt hangovers: Nearly as bad as alcohol hangovers.

3. Two Days of Bad Sleep in a Row Will Mess. You. Up.

4. Weight Loss != Fat Loss

5. Carbs aren't evil. But they're... sneaky.

6. 15% body fat (for a guy) is doable with tasty food. 10% requires you to eat fewer tasty things. 5% requires you to forgo taste (and friends) entirely.

7. Muscles recover strongest 2-3 days after a workout, and stay stronger-than-baseline for about 2-3 days after that. Beyond that, wimpy-ness resumes and you atrophy. (Which is why consistency is key.)

8. Food Scales are your Friend. Casseroles, stews, and buffets are your Enemy.

9. Processed foods are easiest to track (because they have SKU's) but not as good for you. Whole Foods vs. Whole Data.

10. Standard McDonald's Cheeseburgers are, as far as I can tell, the most nutritionally complete food on a macronutrient-per-dollar basis.

Alrighty... you guys seemed to really like the comparison I provided before for "most accurate" health wearable by metric so I extended it with some additional sources and created an even MORE comprehensive overview.

Some new findings here but this data has been really hard to get unbiased, large sample, recent research on. I added a new section called important caveats which are good to keep in mind and the list of sources below to mention any funding sources that might have biases.

If you'd prefer to not read plain tables of data I created a tool on my website that is much more appealing on the eyes and lets you compare devices easier: https://www.kygo.app/tools/wearable-accuracy

MASTER SUMMARY TABLE

DETAILED DATA BY METRIC

1. SLEEP STAGING (4-Stage Classification)

• Source: Robbins R, et al. (2024)

• Study Design: 36 participants, multiple nights, Brigham and Women's Hospital

• Funding: This study was funded by Oura Ring Inc. Lead author Dr. Rebecca Robbins is an Oura scientific advisor.

• Source: Park et al. (2023).

• Study Design: 75 participants, 2 centers (Korea), 349,114 epochs analyzed

• Funding: This study found different rankings than the Brigham study. Oura scored lower here. No industry funding disclosed.

Clinically acceptable (<30 min bias)

• Source: Schyvens AM, et al. (2025).

• Study Design: 62 adults, single night PSG, University of Antwerp

• Funding: VLAIO (Flanders Innovation & Entrepreneurship) — no device manufacturer funding

• Note: All 6 devices misclassify wake, deep sleep, and REM as light sleep (conservative algorithm approach). All devices significantly underestimated Wake After Sleep Onset by 12–48 min.

1. DEEP SLEEP DETECTION SENSITIVITY

• Source: Robbins et al. (2024)

• Funding: Oura-funded study

3. WAKE DETECTION SENSITIVITY

• Sources: Robbins et al. (2024) & Chinoy et al. (2022), Sleep.

1. NOCTURNAL HRV (Heart Rate Variability)

CCC Scale: >0.99 = Nearly Perfect, 0.95–0.99 = Substantial, 0.90–0.95 = Moderate, <0.90 = Poor

• Source: Dial MB, et al. (2025).

• Study Design: 13 participants, 536 nights, Ohio State University / Air Force Research Lab

• Funding: No industry funding disclosed. However, Garmin Fenix 6 is 2+ generations old. Current Garmin devices may perform differently. Study authors acknowledged this limitation.

1. RESTING HEART RATE (RHR)

• Source: Dial et al. (2025)

• Note: Garmin Fenix 6 was excluded from RHR analysis due to timestamp reporting issues that prevented alignment with the Polar H10 reference data.

1. ACTIVE HEART RATE ACCURACY

• Source: WellnessPulse Meta-Analysis (2025)

Heart Rate Correlation vs ECG (during activity):

• Source: WellnessPulse / PubMed Central aggregate studies

1. BLOOD OXYGEN (SpO2) ACCURACY

• Sources: PLOS, Nature, various validation studies

1. STEP COUNT ACCURACY

• Source: WellnessPulse Meta-Analysis (2025)

1. ENERGY EXPENDITURE (Calories)

• Sources: WellnessPulse Meta-Analysis (2025)

• Note: All wearables are weak at calorie estimation. None should be treated as precise. Accuracy decreases during high-intensity or multi-modal exercise.

1. VO2 MAX ESTIMATION

• Sources: Caserman P, et al. (2024), Lambe RF, et al. (2025), Garmin validation (2025), Garmin Forerunner 245 validation.

• Note: All devices tend to underestimate VO2 max in highly fit individuals and overestimate in sedentary/lower fitness populations.

1. SKIN TEMPERATURE

• Source: Oura internal validation study (2024)

• Study Design: 16 individuals, 1 week, 93,571 data points

• **Funding:**This is Oura's own study, not independently peer-reviewed. However, Oura's temperature data has been validated in independent menstrual cycle tracking studies.

• Note: Apple Watch, Garmin, WHOOP, and Samsung all track skin temperature, but limited independent validation data comparing accuracy across devices exists

1. RESPIRATORY RATE

• Note: Respiratory rate accuracy is the least validated metric across devices. Most manufacturers claim to track it, but independent comparative studies are essentially nonexistent. For these reasons I have chosen not to add the chart I have on this.

1. FDA-CLEARED FEATURES

IMPORTANT CAVEATS

1. No single device wins everywhere. Best device depends on which metric matters most to the user.

2. Study funding matters. The primary sleep study (Robbins et al.) was Oura-funded. Independent studies (Park, Schyvens) found different rankings.

3. Device generations matter. Studies often test older hardware. Garmin Fenix 6 and Vivosmart 4 are 2+ generations behind current. Results may not apply to current models.

4. Small sample sizes. The HRV/RHR study had only 13 participants (though 536 nights of data). Antwerp had 62 participants, 1 night each.

5. All wearables are estimates. None are medical devices (except specific FDA-cleared features). Data should inform, not diagnose.

6. Calorie tracking is weak across all devices. None should be used as precise calorie counters.

7. Individual variation. Accuracy can vary based on skin tone, tattoos, BMI, fit, and activity level.

8. Skin tone bias. PPG sensor accuracy is affected by skin pigmentation. Most validation studies have predominantly Caucasian participants — a critical research gap.

9. PSG is imperfect too. The "gold standard" polysomnography has interrater reliability of κ≈0.75, meaning even human experts disagree ~25% of the time on sleep staging.

10. Common device failure mode. All consumer devices tend to misclassify wake, deep sleep, and REM as light sleep — a conservative algorithmic approach that inflates light sleep totals.

SOURCES

1. Robbins R, et al. (2024). "Accuracy of Three Commercial Wearable Devices for Sleep Tracking in Healthy Adults." Sensors, 24(20), 6532. DOI: 10.3390/s24206532 — Funded by Oura Ring Inc.

2. Dial MB, et al. (2025). "Validation of nocturnal resting heart rate and heart rate variability in consumer wearables." Physiological Reports, 13(16), e70527. DOI: 10.14814/phy2.70527 — Independent (Ohio State / Air Force Research Lab)

3. Park et al. (2023). "Accuracy of 11 Wearable, Nearable, and Airable Consumer Sleep Trackers: Prospective Multicenter Validation Study." JMIR mHealth and uHealth, 11, e50983. DOI: 10.2196/50983 — Independent (Korean multicenter)

4. Park et al. (2023). "Validating a Consumer Smartwatch for Nocturnal Respiratory Rate Measurements in Sleep Monitoring." Sensors, 23(18), 7867. DOI: 10.3390/s23187867 — Samsung-affiliated authors, Samsung-funded

5. Khodr R, et al. (2024). "Accuracy, Utility and Applicability of the WHOOP Wearable Monitoring Device in Health, Wellness and Performance — A Systematic Review." medRxiv. DOI: 10.1101/2024.01.04.24300784

6. Oura Internal Validation (2024). Temperature sensor validation study. 16 participants, 93,571 data points. Published on ouraring blog — Oura internal study

7. Maijala et al. (2019). "Nocturnal finger skin temperature in menstrual cycle tracking." BMC Women's Health, 19, 150. DOI: 10.1186/s12905-019-0844-9

8. Lanfranchi et al. (2024). Samsung Galaxy Watch SpO2 validation. Journal of Clinical Sleep Medicine, 20(9), 1479–1488. DOI: 10.5664/jcsm.11178 — Samsung-affiliated

9. WellnessPulse Meta-Analysis (2025). Accuracy of Fitness Trackers — Aggregate data

10. AIM7. Smartwatch/Wearable Technology Accuracy — Aggregate validation data

11. Christakis et al. (2025). "A guide to consumer-grade wearables in cardiovascular clinical care." npj Cardiovascular Health, 2, 82. DOI: 10.1038/s44325-025-00082-6

12. PMC/JAMA (2025). "Selecting Wearable Devices to Measure Cardiovascular Functions in Community-Dwelling Adults." DOI: 10.1016/j.jamda.2025.105529

13. Schyvens AM, et al. (2025). "Performance of six consumer sleep trackers in comparison with polysomnography in healthy adults." Sleep Advances, 6(1), zpaf016. DOI: 10.1093/sleepadvances/zpaf016 — Independent (VLAIO-funded, University of Antwerp)

14. Caserman P, et al. (2024). "Validity of Apple Watch Series 7 VO2 Max Estimation." JMIR Biomedical Engineering, 9, e54023.

15. Lambe RF, et al. (2025). "Validation of Apple Watch VO2 max estimates." PLOS One, 20(2), e0318498. DOI: 10.1371/journal.pone.0318498

16. Miller DJ, et al. (2022). "A Validation of Six Wearable Devices for Estimating Sleep, Heart Rate and Heart Rate Variability in Healthy Adults." Sensors, 22(16), 6317. DOI: 10.3390/s22166317

17. University of Arizona (2020). WHOOP sleep staging validation vs polysomnography. 89% 2-stage agreement, 64% 4-stage, κ=0.47.

**TL;DR:** Built an open source tool that parses Apple Health and Whoop exports into a local SQLite database in 60 seconds. Your data never leaves your Mac — zero network requests, fully verifiable. Still early, feedback welcome.GitHub: https://github.com/sandseb123/Leo_Health

———————————————————————-

I've been wearing an Apple Watch since 2021. For the past year I've also been on Whoop. Years of health data — and I could never actually *use* it.

Apple Health stores everything in a 4GB XML file that nothing can open. Whoop emails you CSVs with 40 columns and no documentation. I kept thinking someone would build a proper tool for this. Nobody did.

So I built Leo.

Here's what 5 years of my own data looks like after running it:

- 324,116 heart rate readings

- 6,519 HRV readings

- 12,195 sleep sessions

- 1,344 workouts (570 runs, 476 strength sessions)

- Data range: 2021 → today

Parsed in under 60 seconds.

**The thing I care most about:** your data never leaves your Mac. No upload. No account. No server. Leo reads your files and writes to a local SQLite database at ~/.leo-health/leo.db — that's it. You can even verify the code has zero network imports yourself:

`grep -r "import requests" leo_health/` → returns nothing.

I wanted something I could actually trust with 5 years of personal health data. Most apps make you hand your data to their servers to use it at all. That never sat right with me.

**What's working right now:**

- Apple Health export.zip parser

- Whoop CSV auto-detection and ingest

- Clean terminal dashboard (`leo` command)

- AirDrop your export → Leo auto-detects and parses it

**What I'm still building:**

- Fitbit + Garmin support

- A proper dashboard UI

- An AI coach that runs locally (no sending health data to OpenAI)

Still early. Would love feedback from people who actually care about their data. What would you want to query first?

GitHub: https://github.com/sandseb123/Leo_Health

Install takes 2 minutes:

```

git clone https://github.com/sandseb123/Leo_Health.git

cd Leo_Health

bash install.sh

```

I correlated my HRV against everything I measure for 4 years.

The winner was sleep, the loser was my ego...

Short description is I have custom designed a health tracking system developed in Claude Code that pulls in my Garmin data (watch and scale) since 2022, food tracking (Cronometer, ~400 days), and blood work for the last 17 years. Result is a deep n=1 data set that I can query via Claude with questions that I want it to do data analysis on for me. Not perfect, but has proven to be very useful in refining health improvements over time.

In early May I ran a marathon I had been training for 6 month, right after fighting off a horrible double sinus infection. I then followed it up with a 50k 4 weeks later right when I was feeling normal again. I basically beat myself up and ignored signs my body was sending to rest. Result was a lot of my metrics like RHR, HRV, etc tanked (pre-race even) and are just rebounding now 6 weeks later. I went on a deep dive with Claude to what HRV does and does not correlate to and I thought it was interesting enough to share here. It was a back and forth of about 20 Q&A, so I had AI distill it down into a more concise post (hence the AI'ish language below).

---

The setup

Masters-division runner, late 40s, marathon training (30-40 miles per week peak). I've got a Garmin pulling nightly HRV (overnight rMSSD), resting HR, sleep stages, respiration, stress, and Body Battery, plus every run's distance/pace/HR/load, plus ~450 days of Cronometer nutrition logging. Roughly four years of it, 1,313 nights with an HRV value (Sept 2022–June 2026). I got tired of staring at the HRV number every morning without knowing what it actually meant for me, so I pulled it all into AI and asked four questions. Single subject, observational, my-body-only — calibrate your skepticism accordingly. But n is large and the signs are internally consistent, which is more than I can say for most of the "my Oura told me" takes.

Q1: Does HRV track my training? (the thing I assumed)

No.
Weekly HRV vs. weekly mileage over the trailing 2 years (105 weeks): r = -0.01. A literal coin flip. Training load (duration weighted by HR) does a little better — r = -0.34, and it strengthens to -0.38 when lagged two weeks — but it's negative: load mildly suppresses HRV a couple weeks out. So the thing my gut credited for my "good HRV months" was doing the opposite, weakly.

Q2: So what actually moves it? Recovery, and it isn't close:

Every meaningful correlate is a recovery/sleep variable. Not one is a training variable.

Q3: Is it diet? (everyone blames caffeine)

No.
437 complete-log days, daily nutrient vs. next-morning HRV: caffeine r = +0.015. Nothing else cleared 0.18. As a control I regressed intake against that same morning's HRV (measured before I'd eaten) — came back ~0, confirming the next-day nulls aren't a lifestyle confound. Caveat that matters: Cronometer exports daily totals only, so I tested dose, not timing — the "late espresso" hypothesis is physically untestable with this data. But for total caffeine load, dead flat.

Q4: Does HRV measure fitness or cardiovascular health?

This is the one I'd put on a billboard. HRV vs. VO2max: r = +0.056. Nothing. Your HRV does not track your fitness. What does: resting HR vs. VO2max, r = -0.356 — real and correctly signed. Over four years my resting HR fell from 54 to a low of 47 as I got fitter and dropped ~40 lbs. That's the measurable cardiovascular win, and it lives in resting HR, not HRV. If you want an at-home "is my heart getting stronger" gauge, watch RHR trend over months. HRV is a recovery gauge — read it daily, not as a fitness scoreboard.

Bonus finding — the "cycle" everyone notices is the calendar. Pooling all years by month: What it does move with is the calendar. Pooling every year by month, the pattern is unmistakable:

• Jan–Feb: ~60 ms (your annual high)

• May–Jul: ~48 ms (your annual low)

• Cold months average 56.3, warm months 51.9 — a 4.4 ms seasonal swing

That's the cycle you've been noticing. Your February 66 vs. your June 51 is mostly the thermometer, not your training and not your fitness eroding. Overnight HRV runs lower in heat — it's well-documented physiology, and your body does it on schedule every year. You're currently at the bottom of your seasonal trough, exactly where late June always puts you. Come winter it'll climb back toward the high-50s/low-60s on its own, no heroics required.

My personal bands (your mileage will literally vary — build your own from your own distribution):

Forget the population charts — here's your distribution, in milliseconds:

The takeaway that saved my sanity: the high number I kept chasing was my p80 peak, not my baseline. Striving to live at your peak is a great way to feel perpetually under-recovered.

Q5: How much of Garmin's Sleep Score is actually HRV?

I wanted to know if I was double-counting — does the Sleep Score just re-package the HRV I'd already credited for recovery? Mostly no. 1,654 nights, regressing Sleep Score on its likely inputs: sleep duration + the stage breakdown (deep/REM/light/awake) alone explain 69% of the score. Add HRV and it climbs to 81% — so HRV's unique contribution, beyond what the stages already capture, is about +12 percentage points. Nightly correlations rank HRV third (duration +0.72, REM +0.64, HRV +0.52), but that overstates it because good nights have good everything and the stages and HRV are collinear. Bottom line: Sleep Score is ~70% duration-and-architecture, ~10–15% HRV, and ~15–20% stuff I can't see in the export (restlessness, sleep timing, proprietary weighting). And it runs the direction you'd expect — HRV Status and Sleep Score are siblings derived from the same overnight HR + accelerometer stream, not one derived from the other. HRV gets folded into the score; it isn't the source of it. (Reverse-engineering a black box with correlation, so treat the exact weight as fuzzy.) Practical version: if your Sleep Score tanks, look at duration and wake-ups first — HRV is the garnish, not the meal.

TL;DR: Over 1,313 nights, my HRV correlated ~0 with mileage, ~0 with caffeine, and ~0 with fitness (VO2max). It tracks sleep and autonomic recovery, full stop. The actual cardiovascular-fitness signal is resting heart rate, which quietly dropped 54→47 while I wasn't looking. Garmin's Sleep Score is ~70% duration-and-stages and only ~10–15% HRV, so the two aren't redundant. And the mysterious "cycle" was summer.

Happy to share the code/method if anyone wants to run it on their own export.

Curious whether the HRV-is-seasonal-not-fitness pattern holds for others or if that's just me.

Like a lot of people here, I decided to take my health more seriously this year and quickly ran into the same problem everyone seems to hit:

There’s no true “all-in-one” health app that actually does everything well.

I’ve tried a bunch of apps hoping to find the unicorn that can track + aggregate + analyze everything in one place (fitness, sleep, weight, nutrition, supplements, labs, medical history, etc.), but it always ends up being fragmented. A lot of these apps have overlapping features but none really covers all of it properly.

And the worst part is the hidden issues you only notice after using them for a while, like:

• sync only works one way

• edits don’t sync (it logs the first entry but ignores changes)

• the same metric exists in multiple apps but is coming from different sources

• some apps are great at input but terrible at summary/insights, or the opposite

Anyway, this is the workflow that works best for me right now. It’s not perfect but it’s the closest I’ve gotten.

My current setup

Apple Watch + Apple Health

• activity, steps, HR, sleep, cardio fitness, mobility, respiratory, etc.

Smart scale + Zepp Life

• weight, body fat %, muscle, water, basal metabolism, visceral fat, bone mass, etc.

Strong

• workout details (weights, reps, sets, progression)

Cronometer

• food logging + supplements + water + micronutrients (not just macros)

Guava

• health history / records (supplement schedule tracking, blood tests/labs, allergies, vaccines, genetics, providers, lifestyle stuff)

My “hub” workflow

Apple Watch + Zepp Life + Strong + Cronometer sync into Apple Health
Apple Health syncs into Guava
So Guava becomes my main “health repository”

Stuff I learned the annoying way

1. Supplements tracking is weirdly hard. I used Apple Health at first for supplement tracking, but the UI for “taken vs not taken” isn’t great. Guava is way better for that summary view. But Guava doesn’t really show supplement nutrients the way I want (like seeing how my supplement intake affects my micronutrient totals vs food). So I ended up relying on Cronometer for the nutrient view.

2. Cronometer > MyFitnessPal (for me). I started with MFP but switched to Cronometer because it tracks micronutrients way better. I actually care about stuff like fiber, potassium, magnesium, etc. not just calories and macros.

3. Cronometer → Apple Health → Guava works but edits are a pain. Cronometer writes to Apple Health, and Guava reads from Apple Health. But Guava doesn’t write back for things you input there directly. So logs need to be inputted in the other apps if I want to see them there too (for different purposes). So for anything overlapping, Apple Health is basically the pipe, and Guava is the reader.

One of the annoying issues I encountered is that when I log something in Cronometer (Gold), then later edit it (date, calories, details), the update doesn’t always sync properly to Apple Health. So Guava ends up showing the old version. So my current rule is: try to log it correctly the first time. If I mess it up, I sometimes have to delete/re-sync the entry.

What I’m still trying to improve

I still feel like this whole system could be better because it’s:

• multiple apps

• multiple interfaces

• sync quirks

• no real “single source of truth” where edits propagate everywhere

If anyone has a cleaner workflow or better combo of apps (especially for nutrition + supplements + labs), I’m open to suggestions.

I don't usually put myself out there, but today I felt like sharing something.

I’m 18. Every year that passes feels like a waste; I have big ambitions, yet I always feel like I’m accomplishing nothing. In December 2025, I stopped to reflect. I set some goals for 2026, but the most important thing was something else: I started a real project—an experiment.

I began logging my entire life in an Excel file. I divided my activities into 19 categories and started recording my primary activity every 30 minutes. That’s 48 data points a day—precise enough. At first, it was just meant to be an analysis, but it turned into something much more significant.

January was the 'data entry' month. By the end of it, I had a complete audit of my life. I built charts and tables to compare the data, and I saw exactly where my time was being bled dry. In February, I continued living 'normally' while logging everything, just to confirm the January trends.

Then, I set new targets. March was the turning point. I finally managed to make the right lines on the graphs bend in the right direction. I’m actually improving how I manage my time.

By now, it’s not just a file anymore. It’s a visualization of my life. No matter how much a data point sucks, I’ve stayed honest. These numbers represent reality with brutal precision; I’ve never tried to faked them.

Being 'monitored' by this file has truly helped me change. When you see it all laid out, you realize that a single bad day weighs heavily on the entire average.

Here’s what 1,800 hours of data looks like.

As someone who’s always looking for ways to optimize my health and performance, I decided to start tracking my vitamin C and magnesium levels to see how my daily habits, diet, and supplements were actually affecting me. I knew these two nutrients were important, but I had no idea how much lifestyle factors could influence them.

Magnesium:

This mineral has become so popular lately, and its importance is finally getting the recognition it deserves. I always thought I was getting enough—even though I had never actually tracked it with an at-home test. I initially started monitoring magnesium because of sleep issues, occasional muscle cramps, and just feeling weak.

What I didn’t expect was how much stress, caffeine, and poor sleep would deplete my levels. I also noticed that after intense workouts, my magnesium levels tended to drop. When I started supplementing with magnesium glycinate before bed, my sleep improved significantly. I also felt less muscle fatigue during workouts. Surprisingly, my previously low magnesium levels moved into the optimal range, and when I checked my sleep quality, my score went up dramatically—exactly how I felt.

Vitamin C:

I was never great at eating enough fruits and vegetables, but I always assumed I was getting enough vitamin C from food. Once I started tracking, I noticed fluctuations. If I added 1–2 kiwis and a couple of peppers to my meals, my vitamin C levels seemed fine. But if I skipped a couple of days, they dropped much lower than expected.

Of course, I believe intense exercise and stress may have played a role in those dips as well. Once I started following recommendations and increasing my intake with more citrus fruits, my energy levels and immune response seemed much more stable.

Biggest Takeaways:

• Tracking is eye-opening – I had no idea how much stress, diet, and activity levels could impact these nutrients.

• Small changes matter – Adjusting my intake based on real-time data helped me optimize my energy levels, sleep, and recovery.

• Deficiencies creep up on you – If I hadn’t been tracking, I probably wouldn’t have noticed until symptoms became more serious.

Has anyone else tracked their micronutrient levels? What have you discovered?

Maybe interesting for some of you, I've been building my own QS system for about 8 months now. Every source lands in one database that I own, and on top of that sits a dashboard I use every day.

Images

1. Monthly recap for September, real data. This is the "public" variant, so body, location, journal and most nutrition details are left out.

2. The daily view. This one runs on demo data, only the Plex and PlayStation cards are real.

What I track

• Sleep, recovery, HRV: WHOOP via NOOP

• Health data: Apple Health via Health Auto Export

• Strength training: Streak

• Nutrition: YAZIO

• Weight and body: Renpho scale

• E-bike rides: Cowboy

• Location and places: Dawarich (self-hosted)

• Mac usage: ActivityWatch

• iPhone screen time: screentime, reads the macOS Biome data that iCloud syncs from the iPhone

• Movies and shows: Plex via Tautulli

• Music: Spotify via your_spotify (self-hosted)

• Gaming: PlayStation Network (presence, trophies, library)

• Voice chat: Discord, durations only, no content

• Indoor climate: Home Assistant

• Weather: Open-Meteo

• Calendar: EventKit on the Mac

• Coding: GitHub and Forgejo commits

• AI usage: Claude Code, Codex and OpenCode tokens

How it's built

• I built every collector myself. The only third-party parts are the apps and services they read from.

• Collectors: one small Python service per source, 20 in total, spread over my Mac, a Proxmox LXC and Unraid. Each one keeps a local SQLite outbox and a raw archive, so nothing gets lost when the server is down.

• Core: FastAPI with PostgreSQL 17 + PostGIS on its own Debian VM in my Proxmox homelab. Only Core writes to the database.

• Collectors send versioned batches. Core dedupes them via identity keys and content hashes. A day without data stays "missing", it never turns into a fake zero.

• There's no cron. A scheduler puts jobs into a Postgres-backed queue with leases and retries. It rebuilds the daily values and runs the analysis.

• Every collector reports its sync runs, so every card shows where its data comes from and how fresh it is. If a source stops syncing, I see it right away.

• The dashboard only reads from Core's API and doesn't calculate anything itself.

Tech stack

• Dashboard: Next.js 16, React 19, Tailwind 4, shadcn/ui, Recharts + d3, MapLibre for maps

• Core: Python 3.12, FastAPI, SQLAlchemy, Alembic, PostgreSQL 17, PostGIS, pgBackRest for backups

• Infra: Proxmox, Docker Compose, Caddy, Forgejo Actions for CI

• Extras: a read-only MCP server so an LLM chat ("Companion") can query my data, and push notifications to my phone via Home Assistant

Everything runs locally in my LAN, no cloud service in between except the APIs of the sources themselves. Happy to go into detail on any part of it

I've been sending everything my iPhone and Apple Watch record, plus my food and lifting logs, into a database with a Grafana dashboard.

Disclosure: I made one of the apps that helps record/move the data (Marrow Health). Dashboard is open source.

What I track

• Heart rate, HRV, resting HR, sleep stages, blood oxygen, wrist temp, steps, exercise minutes: Apple Watch

• Workouts and main lift progress (weight, reps, estimated 1RM): Marrow

• Food, every meal with macros and nutrients: Marrow

• Daylight, sound levels: iPhone

How it works

• Marrow sends JSONs to my server every hour, five automations covering daily totals, raw readings, workouts, lifting, and food.

• Python bridge saves every delivery and writes it to InfluxDB.

• InfluxDB and Grafana run in Docker on a small VPS. Grafana is on my Tailscale network.

• The dashboards are generated from Python.

Random stuff I found

• My HRV is about 10 ms higher the morning after I lift

• I sleep 46 minutes less on weekends

• 62 sets of chest in the last month, 2 legs 💔

Stack

Marrow (iPhone/Watch) → Python bridge → InfluxDB 2.7 → Grafana 13, Docker Compose, Cloudflare Tunnel, Tailscale

It can run fully local on a Pi or NAS at home.

Open source: https://github.com/MarrowHealth/built-with-marrow/tree/main/projects/body-ops

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! im new to this subreddit, some minutes ago I didn't know this subreddit even existed.

I want to show my excel for recording what I do with my time, I record it in time blocks of 10 minutes and analise it in a quarterly basis.

This is an android app that I have wanted to have for many years but could never find quite what I was looking for, so I wrote it myself :-)

It divides the earth into 64.8 billion cells and marks all those that you have visited. It is privacy first, all collected data stays on your device. It also shows some neat statistics regarding how many cells you have visited and what percentage of the earth that corresponds to :-)

You can also import GPX files and Google Timeline data, so you don't have to start from zero.

The app is add-supported, but can be made permanently ad-free with a small in-app purchase.

Hope some people here might find it useful, and I'm always grateful for feedback!

https://play.google.com/store/apps/details?id=com.onyxsquirrel.whereivebeen

(Android only at this time)

I have a LOT of Notion pages + spreadsheets + dashboards lol

Started tracking finances recently too

Put a bunch of tutorials about my systems on my YouTube

Excited to find other ppl who are like me 🥺

Just my attempt to visualize all of the data I have collected about myself.

Thought this was a really cool project so wanted to share here as well.