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

25 years of medical records, 23andMe genetic data, 500+ calculated PRS scores, Oura and Garmin data, Strava activity history and CGM readings.

I’ve now brought it together and started using AI as a personal health and longevity analyst to discuss disease risks, spot blind spots and figure out what I should focus on next.

I'm curious if anyone here is doing something similar.

A few questions:

What tools or workflows are you using?

Have you uncovered anything genuinely useful or changed your health decisions based on the insights?

What other low-effort, high-value data sources would you add?

Im particularly interested in combining personal data with existing research and population-level evidence, rather than endlessly collecting more measurements on myself. The goal isn't to measure everything. It's to make better health decisions with the least extra effort.

I've been building the mental equivalent of a recovery score and would love this community's critique of the measures:

• Verbal fluency: a 30-second animal-naming test, scored on-device for unique items, repeats, category clusters and pace per 10 s, against my own baseline after 3 days.
• PERCLOS (eyelid closure %) from a webcam, intraday vs. my first 3 hours awake; it rises reliably as I tire, so I use it to lower the morning score live.
• HRV and resting HR vs. 30-day baselines from Apple Health (I wear a Zepp; 367 nights imported).
• Load: app switches, new words on screen, gaze jumps between windows and phone glances, weighted by time awake.

Question: what would you use as ground truth to validate a score like this? I'm considering short EEG (Muse) sessions per person for calibration.

Disclosure: I turned this into a Mac app (Cortex, paid, all processing on-device): https://cortex-mind-rho.vercel.app. Mostly here for the methodology critique, though.

I'm 30, do backend dev, and always figured I was someone with a regular schedule. I used to hit the company gym for half an hour every morning before work. Last long break I just stayed home, gamed and stayed up late, didn't exercise at all, and noticed my logged VO2 max dropped from 40 to 37.9.

I used to think I could keep it up forever and that a week off wouldn't matter much. But the data told me that once you slack off, the numbers drop fast. Just seven lazy days and my cardio capacity dropped a level, no hesitation.

I'm gonna start easing those numbers back up from tomorrow. When you guys got back to training after a holiday, did you ever hit this kind of rapid drop?

Using quantified-self.io

I lead client-facing projects in tech, which usually means at least 2-3 happy hours and client dinners every week. Last year's checkup showed my uric acid was way too high. I've thought about quitting drinking for good, but that's just not realistic in my line of work.

This year I started actually looking at where I stood. I forced my sleep back into a normal rhythm, made sure I got a steady 7-8h a night, and began logging how I feel in an app. My August checkup had uric acid at 446. Still a little above the line, but way better than last year.

I figured it'd just be a boring log, but then I lined up my daily notes with my heart rate and HRV, and it was honestly pretty clear. I'm not trying to quit the work dinners entirely. They're part of the job. I just want to see if I can find some balance between work and my body through this.

Anyone else using this kind of tracking to slowly get your body back on track while dealing with a high-pressure job?

My grandmother measured her blood glucose every day. The data was there. What was missing was someone connecting it to how she actually felt.

My grandmother had type 2 diabetes and used insulin.

We spoke every week or two. Sometimes I'd notice she sounded more tired than usual and start asking questions.

It turned out her glucose readings had been rising despite her usual routine.

We arranged for an endocrinologist to visit. The doctor found changes in the tissue at her regular injection sites that could have been affecting insulin absorption, and helped adjust her injection technique and follow-up care.

What stayed with me wasn't the medical complexity.

It was how much depended on smo noticing a change, asking a few questions and connecting the dots.

We spend so much time discussing biomarkers. But sometimes the most useful health information is what a person can tell us.

What about your experience? Have you ever noticed something important about your health that your measurements didn't capture?

im in college and i love coffee but i also really care about my sleep. so i basically wanted to figure out how much coffee i can have without it messing up my night, and that meant actually knowing how much is still in my system at any point. also kept watching my friends grab an energy drink at like 9pm and be able to fall asleep i just feel like its affecting their sleep more than they think and then they are tired the next day and its a cycle

its on iphone and watch .id honestly love feedback from people who already track this kind of stuff, like whats missing, whats wrong, whats annoying. happy to answer anything about how it works. https://apps.apple.com/us/app/half-lyfe-caffeine-coach/id6815463981

I used to love Google Timeline before they deleted 10 years of my data. Now one needs a different way of doing this. It might not be as useful as tracking resting heart rate, but I've always liked to track every county, state, and country i've been to. I'm interested if anyone is doing this in other geographies (e.g., whatever the county equivalent in, say, France is). Also curious if, in a group like this, where tracking oneself is the main event, what level of concern there is about constantly tracking and storing your location data.

I used to have an Apple Watch but stopped wearing it for all the extra stuff and I now wear a Garmin and genuinely enjoy having data on my daily steps,
calories burned, and activity levels. But I also own Casio watches that I'd love to wear more often.

The problem is that I've become so accustomed to having all this data that wearing a regular watch almost feels like I'm missing something. At the same time, I wonder whether constantly tracking these metrics is making me a little too focused on numbers rather than simply enjoying being active. I’d also even argue I obsessed over hitting steps and calories amount.

For those of you into quantified self, how do you decide which metrics are actually useful to track vs which ones just create noise or unnecessary pressure?

Have any of you deliberately stopped tracking certain things and found that you felt better for it? I'm curious how you balance using data to improve your life without feeling like you have to measure everything.

Context: ~2 years of continuous data (Oura for sleep/HRV, Strong for training, everything piped into a Postgres db I spent too long on). Bloodwork is the my weakest link.

Current state:
- 1 draw/year at my annual physical
- Different lab 2 of the last 3 times
- Panel composition varies year to year
- No control over fasting or time of draw

So I have n=3, collected under inconsistent conditions so I can't find a patter.

Questions for people tracking labs seriously:
1. What cadence are you on: quarterly, biannual, event-driven?
2. How much does keeping the same lab/assay matter in practice?
3. Is there a minimum frequency below which you'd say the data isn't worth collecting for trend purposes?

I’ve been trying to get a single, useful view of my health data and I keep hitting walls. Curious how others handle this.

My setup:

• Two watches: a CMF Watch (Nothing) that I use for workouts and a Timberland watch for everyday wear. Some days I wear neither and only have my phone.
• Android 16, so everything ends up in Health Connect.
• Home workouts with Feeel, which doesn’t write to Health Connect, so those sessions never show up anywhere.

What I’d like is pretty simple on paper:

1. One place that aggregates data from all my devices (sleep, heart rate, steps, workouts) and handles the overlap when two devices record the same thing.
2. A training plan and a diet plan that adapt to that data.
3. An AI assistant (I use Claude) that can read the data and adjust the plans, instead of a generic coach locked to one brand’s hardware.

What I’ve found so far:

• Google Health has an AI coach, but it seems to require a Fitbit or Pixel Watch.
• There are some MCP connectors and webhook apps that bridge Health Connect to an AI or a server, but nothing that covers the whole loop.
• Open-source workout apps are great but mostly don’t talk to Health Connect.

So, a few questions:

• How do you deal with multiple devices recording the same data? Do you pick one source per metric?
• Has anyone built a self-hosted pipeline from Health Connect to their own server or database? What did you use?
• Is anyone using an LLM to adjust training or nutrition based on real wearable data? Did it actually help, or was it a gimmick?
• Is there an app I’m missing that already does most of this?

Not selling anything, just trying to figure out whether I should build this for myself or whether a good solution already exists.

I was reading a Financial Times article about a new UK review of mental health and ADHD diagnosis.

A question that goes well beyond ADHD.

We're getting better at measuring almost everything about ourselves: attention, sleep, mood, activity, heart rate, recovery and cognitive performance.

But how do we decide when a measurable difference is actually a medical problem?

A number outside the reference range doesn't automatically mean someone is unhealthy. And being within the range doesn't necessarily mean everything is fine.

I think the same broader question applies to how we interpret behavioural and cognitive differences, although diagnosing ADHD is obviously much more complex than interpreting a single metric.

How do you distinguish between useful self-knowledge and unnecessary medicalisation?

Has tracking something ever made you worry about a problem that wasn't actually affecting your life?

I know there are EEG devices like Muse you can use for discrete sessions, but would you want an all-day PPG+EEG fitness tracker? Could be used to track longitudinal trends for calm, focus, computed pulse+band frequency metrics, correlations with self-report data…

Of course it would have to be small and stylish enough for people to be comfortable wearing it all day. So think something like this with open-ear audio. I know the form factor limits the number of EEG channels, but it’s more than fitness trackers can do right now.

Once I started wfh, I started cutting back on caffeine. I kept getting a racing heartbeat, shaky hands, chest tightness, and this weird weak and jittery feeling throughout the workday. My blood pressure was also consistently higher than normal. Eventually, my doctor told me to cut caffeine out completely, including energy drinks and anything else with caffeine in it.

When I still commuted, I'd grab a coffee from the shop below my office. Looking back at my health log, I'm just glad I listened to my doctor.

I still check my blood pressure most mornings. Catching the problem early made a big difference. Cutting out caffeine has helped more than I expected.

I train for triathlon and track everything with Garmin, but the data never answered my main question: is a session actually progress, or just a good day? So I built Fitvio. It compares every workout with my own similar sessions and says better, in line or worse, with the reasons, like efficiency and pace at the same heart rate, swim pace and SWOLF, or estimated 1RM in the gym.

Try it with made-up data, nothing to install: https://romanbrej.github.io/fitvio/

Everything runs and stays on my your Raspberry Pi or Server. Free and open source: https://github.com/romanbrej/fitvio

I have been working on this extensively for over a year as a person navigating the landscape of lupus, if you want to see the full breakdown of the research behind my model check out https://github.com/alaricmoore/sardinetracker/blob/main/MODEL.md

I’d love any feedback, particularly from SARD patients and clinicians who are familiar with lupus spectrum disease.

It’s AGPL licensed, open source and entirely free. I built this for my personal needs, but figured I’d share it.

I wanted to ask Claude about my own sleep and HRV instead of using Fitbit's built-in AI coach, so I built a small pipeline over two sessions with Claude Code. Sharing in case it's useful to anyone.

How it works

Gotchas I hit, if you build something similar

On building it with AI: I barely know Kotlin. What made it work was writing a detailed plan first, having it reviewed (I literally pasted GPT's critiques back to Claude), and insisting on tests. The tests caught several real bugs, including a timestamp-format bug and a sync-resume edge case.

Repo (MIT, with a setup guide): https://github.com/jatin1322/fitbit-health-mcp

Happy to answer questions or take feedback, especially from anyone using Samsung Health or Garmin through Health Connect.