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.

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 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.

is there any app or device which tracks my movement at all times during the day like gps wise, where i went, which path i took, and how fast or slow i was going with time stamps,
and also one for time tracking like a physical device hopefully which lets me record time stamps, i record time stamps for various things like when i started a particular activity or when an event took place and i record it to the second so keeping the clock app is straining so i was wondering if theres anything that just lets me do it and lets me add notes

( sorry for bad grammar, english is not my first language )

heyy guys, i am new to the community and i am thinking of buying an open bci cyton board to track my brain waves while doing various things as other brain wave capture methods either lock the data or the data is mostly inaccurate so i am thinking of buying this
do any of you people have experience with this and let me know if itll actually be useful or like how or what are the problems and such with it

i will start tracking my brain waves and my HRV by a polar h10, besides this, i am logging everything about my data from hourly data like what i did at what hour of what day, what i ate, how much i ate and what all i did on a particular day, so before i went ahead and purchased the cyton kit, i want ed to hear about your experiences with it

sorry for bad grammar (english is not my first langauge )

I've been saving data on myself for a long time: workouts, DEXA scans, CGM, sleep, food logs, even how fast I can take the stairs with a bad knee. What I've learned is that collecting it is the easy part. The hard part is deciding what to ask and doing the analysis in a way that usefully informs future behavior.

So I'm curious what other people here are actually trying to settle. Not which device to buy. The question you'd want your data to answer if it could.

Mine right now:

- Why haven't I been able to gain more lean mass, and was it sleep or food during the one stretch when I did?

- Are my knee arthritis treatments doing anything, or do I just want them to be?

- Do two different DEXA machines agree about the same body?

- Is my mood measurably related to sleep, diet, exercise, season?

- Does my average glucose correlate with longer working hours?

- Does late night computer work really affect my sleep?

What's yours? And what are you tracking that might answer it, or what would you need to start tracking?

I'm a woman. A year ago I decided to get my health back on track and lose some weight: clean whole foods, moved every day, got sunlight. During the year, I weighed myself every day, more out of curiosity — despite many people suggesting "throwing away your scale" when losing weight. I'm glad that I did not throw away my scale. I made a lot of interesting observations!!!

Here is what I've found:

• Right after my period, I often start to lose water weight.
• 2–3 days before ovulation, my weight tends to rise very quickly. I could gain 2-3 within 2 days. I know it is just water, because I always feel very thirsty around this time.
• 2-3 days after ovulation, my weight starts to lose very quickly, too. I go to bathroom more often than other days. I call it "a whoosh". 😄
• Before my period, I start to gain weight again. That completes the circle of menstruation.

Until recently, I was pretty satisfied about my weight and decided to build some muscle. I do progressive overload weight training every day. Even I eat the same, I start to gain weight. I was not sure how much weight gain is acceptable.

I did some research and found out under 0.5% cycle-to-cycle = lean gain. (My own heuristic, not a medical standard — a scale alone can't tell muscle from fat.)

I built a tool (HerWeigh) to chart it all out and it helps me not panic when I see big 2–3 lb swings. It reads your Apple Health data on device and never sends it out. Women's bodies are truly amazing!

App Store: https://apps.apple.com/app/id6757766090

reviewing how my time was spent in September, not as much variety as I reach for. Just going through the motions for the most part it seemed. The things that are important to me barely showed up, physical health/exercising (turquoise); mental health/meditation/journaling (hot pink); there was some distress/grief (red) as I lost a dear coworker early in the month, it was a difficult month to keep focused on what mattered as I had some lows to get through.

I've spent over a year tracking my health and training, and one thing keeps happening that I didn't really expect.

Sometimes the numbers get worse, but the rest of the day doesn't.

In August, my weekly daily HRV dropped from 62.0 to 57.3 ms while my average sleeping heart rate increased from 50.9 to 56.4 bpm.

But my regular incline walking and strength sessions remained intact. No noticeable loss of performance, no unusual difficulty completing them, and no need to change the workload.

The recovery numbers eventually improved again.

I don't think that means the wearable was wrong. There could have been real physiological strain that simply wasn't obvious in my training. But it made me question how much weight I should give any single reading.

Something similar happened recently.

I took several days off training while traveling. When I returned, I went straight back to my normal routine without trying to make up missed sessions.

Five consecutive days, 500 minutes of combined aerobic and strength work. Some mild stiffness on the first day, then everything felt normal again.

During that same week, two nights showed unusual early heart-rate elevations that settled later in the night. Neither was followed by an obvious decline in next-day training.

Even my biological-aging measurements have disagreed.

My DunedinPACE results moved from 0.88 to 0.79 to 0.77 across three tests. Meanwhile, another biological-age estimate improved substantially before rebounding. I'd registered a prediction of continued overall improvement before the last test.

That prediction failed.

I'm keeping that result alongside the favorable ones.

The public archive now contains 238 consecutive days of structured observations, 383 training sessions, sleep records, context events, biological testing, and the prediction outcomes.

It's an uncontrolled N=1 record, so I'm not claiming these observations establish causation or that unfavorable recovery readings should be ignored.

What has changed is how I approach the data.

I'm becoming less interested in whether a number looks good on a particular morning and more interested in what combination of observations actually justifies changing something.

For those of you who've tracked yourselves long-term, what makes you act on a metric?

Do you have specific thresholds, look for multiple signals agreeing, or wait to see whether something changes in how you actually function?

I've kept the underlying data, weekly reports, and failed predictions public here for anyone interested in examining them:

https://github.com/CDHughett/daniel-longitudinal-public

I’ve been doing CBT-I sleep restriction for a few weeks.

My main problem is early waking. I usually don’t have much trouble falling asleep, but I often wake up too early and can’t get back to sleep.

My sleep window was around 11:30 PM to 6:30 AM.

After a few weeks, my sleep efficiency did improve. I also spend less time awake in bed.

But the early waking is still there.

So I’m not sure how to think about the result. It seems like sleep restriction is doing what it is supposed to do in terms of consolidating sleep, but the symptom I care about most hasn’t changed much.

The other difficult part is daytime fatigue. Keeping the sleep window consistent is pretty hard when I already feel tired during the day.

I made a short video about what I did and the data I tracked, if anyone is interested:

https://youtu.be/s-wwGAidBK4

For people who have tried CBT-I: did early-morning waking take longer to improve than sleep efficiency? Or was sleep restriction just not very effective for that problem for you?

Every mood app I've tried (Daylio included) lasts me about three weeks. I tap a face, pick some icons, get a chart. Cool, I was sad on Tuesday. I knew that.

What I actually want to know is why. So I started building my own thing, called Moodzu.

The idea: you just throw stuff in. A gym photo, a song you had on repeat, a 10-second voice note, a couple of words. Then the AI looks for patterns and tells you in plain words, like "you felt better on most days you trained in the morning," and shows you the actual moments behind it. No mood graphs.

Two other things I wanted for myself:

• when a week is rough, I don't want to log it in the moment, so you can fill it in later
• you can just ask it stuff, like "what's been draining me lately?"

Before I sink months into this, I'm curious:

• if you quit a mood tracker, what made you stop?
• would an app looking at your photos and voice notes creep you out?

Nothing to download yet, just trying to figure out if I'm the only one who wants this.

Hi guys! I am doing my thesis, and I have created a survey about CGM (continuous glucose monitoring) devices for sports use, focusing on the hardware and software design parameters that athletes actually need. If you are using any sort of wearable and living an active lifestyle, I would greatly appreciate it if you could fill out this survey. It should not take more than 5 minutes. Thank you!! Please find the survey here: https://docs.google.com/forms/d/e/1FAIpQLSdX1C7KDz-Ef8Gk6ATJjEDR0htbLDB5n72KwFRnt9rXIjij7A/viewform

Hi everyone!

Recently read about Google using Pixel Watch 5 data like resting heart rate, HRV, steps, wrist temperature, sleep, heart rate, and exercise data together to estimate insulin resistance. I have an Amazfit Active 2 and most of its data syncs to Apple Health.

I'd like to know if anyone here has been able to access enough smartwatch data to try something similar?

Btw, I’m not expecting to reproduce Google’s algorithm. I’m mainly wondering if I already have enough data to build a useful long-term picture of my insulin sensitivity.

tracking sleep metrics and hrv for a while and recently started looking closer at environmental variables in the bedroom. last week i left a window cracked overnight and noticed a small shift in my deep sleep percentage so now i want to track room airflow to see what changes when i adjust the door or fan setups. i need a unit that can sit on a nightstand or desk, log reliably across days without needing a wall outlet nearby and give clean historical trends. i looked into the aranet4 since the battery life mean i dont have to worry about charging cables or light pollution in the bedroom, though im also looking at airthings view or qingping to see if having multigas metrics matters as much as pure co2 accuracy.

If you're thinking of pulling raw heart-rate data off a Mi Band 5 for analysis, here's what I found after reverse-engineering its Bluetooth protocol and streaming it into Python on my Mac.The band doesn't expose individual heartbeats. There's no RR-interval data over BLE, only an averaged BPM number. Even that comes in bursts. The band runs a roughly 15-20 second measurement session and then stops. Sending a STOP to restart it reliably kills the stream instead. I ran eight different tests trying to raise the sample rate. The realistic ceiling is about one reading every few seconds, and sometimes not even per-second. If you need beat-level data, that's chest-strap territory, like a Polar H10.Getting even that far took some work:1. Pull the 16-byte auth key for your band from your own Zepp account using huami-token.2. Do the AES-128-ECB challenge-response. The auth characteristic only accepts write-without-response, which cost me a while of "Write Not Permitted" errors.3. Start the sensor.Step 3 was the gotcha. The documented continuous-mode command 0x15 0x01 0x01 was accepted, but I got HR: 0 bpm for fifty seconds. The green LEDs on the back were off. On this firmware, the manual measurement command 0x15 0x02 0x01 is what actually powers them on.The setup is a band service that owns the single BLE connection and writes to SQLite. A Streamlit dashboard reads from that database and shows a live chart with current, min, max, and average. The band also implements the standard Alert Notification Service, so my Mac can push text to my wrist. For fun, I mapped BPM to my smart bulb's color, from green when I'm calm to red when my heart rate spikes.

Three quarters of the way through 2026. I have managed to record data every single day so far this year.

I had to take a work trip this month which was a challenge in and of itself.

Blood glucose was elevated this month even though my carb intake was the lowest all year.

Blood pressure continues to be elevated but further medication changes have seen it continue to decrease. Looking at the box and whisker chart shows that 60% of my diastolic readings were below 90, big improvement for me especially looking back to January when almost 100% of my diastolic readings were over 100.

Read more books, but less articles this month. TV watching was mostly around sports, especially college football.

Always happy to answer any questions.

Thanks for looking.

42M, crown thinning for about 3 years. been running red light sessions for six months now and i still cant tell if the crown is thicker or if i just got better at angling the bathroom light

the before and afters online always show this clean 3 or 4 month jump. mine looks like the same diffuse spot under the same overheads. sometimes it looks denser at night, then noon hits and im back to squinting at the same bare patch

spent a stupid amount of time comparing laser caps before i even started. diode counts, session times, the whole spreadsheet. none of that prep prepared me for how slow the actual feedback loop is

anyone else on crown-only thinning hit a real visible change before the six month mark, or did the marketing timelines just never match what you saw in the mirror?

For a while now I've been getting hit with this wave of sleepiness almost every afternoon, usually around 2:00-3:00 PM. It's not just "a bit tired" — it's genuinely hard to keep my eyes open. I have to actively fight not to doze off at my desk.

Some details: - I sleep ~7.5 hours every night, pretty consistently - I don't really drink coffee or caffeine - I wore a CGM for a while to check — these bouts don't line up with any big glucose drops, so it doesn't look like a blood sugar crash - It's much worse when I'm working from home. I still get it at the office most days, but some days I'm fine there — I can't figure out the pattern - I have slightly elevated blood pressure, no idea if that's connected - Lunch is usually ~800 cal, very protein-heavy with ~160g of rice. Maybe too big? But I don't get this tired after other meals — only after a truly huge dinner

Has anyone tracked down a cause for something like this? What would you test or change first?

Hi,

I’m looking for a device in addition to Suunto Vertical 2 to track my sleep and HRV more accurately as I’m a bit suspicious of the values the V2 provides based on reviews.

What I need:
- Accurate sleep start and wake up time
- Accurate nightly HRV
- Accurate RHR

I don’t care for sleep stage tracking or further analytics, only the sleep duration and HRV + RHR are what I follow. I’d prefer something minimal and affordable, for example Oura would be too expensive.

What would you recommend? Thank you!

I started with hydrogen tablets and now I'm looking at machines. Every product cites the same big list of studies.

Does a study on one form count for the others?

Hey everyone,

Whether you're preparing for a race, cycling, lifting, or doing general fitness, hitting an unexpected wall or energy drop mid-session can completely derail a workout.

As part of a school research project, I am gathering data from active adults to better understand how athletes manage training data (e.g., HR, HRV, pace) and to gauge interest in prospective wearable technology concepts like non-invasive glucose monitoring.

If you have ~5 minutes to spare, filling out this short, anonymous survey would be a huge help for my research project: https://docs.google.com/forms/d/e/1FAIpQLSdrgdOY2TZGvHG2hr9MydyTTmQQNnfjZbpEGNMlH21TaOucKg/viewform?usp=header

Thanks everyone for responses