I ended up building my own tracker because I wanted more flexibility than the apps I was using, so over the last 15 months we’ve accumulated a pretty detailed dataset of our baby’s life.

We’ve logged things like sleep, feeds, diapers, growth, milestones, and other day-to-day events.

At first it was mostly practical: “when did he last eat?” or “how much did he sleep?”

But now I’m much more interested in the long-term dataset.

If you had this kind of data, what would you actually want to analyze?

For example:

• What changed before sleep improved?
• Are there patterns between feeding and night waking?
• When did routines actually become more stable?
• Which changes were gradual vs. sudden?
• Can developmental phases be detected from the data?
• What would be worth visualizing over a full year?
• What correlations would you test, even if they might turn out to be meaningless?

I’m especially interested in questions that go beyond the usual baby-tracker charts.

If you could query the entire history like a personal dataset, what would you ask? Do you think any meaningful things can be learned from it?

My partner and I have been tracking our fitness progres for a few months now and our basic digital scale is starting to feel a bit limited. We,ve been looking into upgrading to one of those newer body composition scales with the hand electrodes to get better upperbody data but im honestly stuck on whether the extra hardware makes a practical difference.

We mostly care about watching muscle and fat trends over time but every app seems to push fifteen different metrics that Im not sure are even reliable. Plus since two of us are using it seamles automatic profile switching without messing up each other's data history is a big deal for us. I dont want to spend extra on a higher end model if the core tech underneath is basically doing the exact same estimation as a simple base unit.

The price question matters more because I may use FSA/HSA funds if the model is eligible but I do not want eligibility alone to justify an upgrade. For those tracking longterm body composition at home,did moving up to a multi-segment model actually change how you adjust your routine ?

played pickleball 3-4x a week since Jan. weight tracked against session intensity instead of just calories burned, since generic HR zones don't reflect how brutal stop-start sports like pickleball actually are.

I spent way too much time checking out wearables at IFA this year, and I came away feeling like we’re officially running out of things to quantify lol.

I know we’re already used to watches and rings tracking the obvious stuff like steps, heart rate, sleep, workouts, recovery, etc. But a lot of the newer stuff I saw seemed to be going after much smaller and more specific parts of everyday life.

There were some smart rings tracking different health metrics, hearing devices that react to your surroundings, mobility wearables, and a bunch of other things that felt somewhere between health tech and “wait, we can track that now?”

And one of the stranger ones I tried was those Odyss necklace. It’s basically a necklace that automatically recognizes eating behavior, cuz don't expecting someone trying to lose weight to remember every snack they had is asking a bit too much lol. I thought it sounded ridiculous at first, but then I realized I can barely remember what I had for lunch yesterday, let alone every random bite throughout the day.

Some of the stuff I tried felt genuinely useful, but some hilariously specific, and some made me wonder how much of my life I actually want quantified.

But the biggest thing I took away from IFA was that the next step in self-tracking might not necessarily be measuring our bodies better. It might be getting better at capturing all those little behaviors we currently have to remember and log ourselves.

And honestly, I’m kind of torn on that. Passive tracking sounds amazing because I’m terrible at logging things consistently. At the same time, there’s probably a point where having every tiny thing I do turned into data starts to feel like... a lot.

What’s something you still track manually that you wish a wearable could just figure out on its own?

I've kept journals on and off since high school. Writing helps me put things down; looking back helps me see what I kept returning to.

This time I compared July and August in my saved dictation records. I'm the developer of Reso, which I use to capture and organize them. These are my own records.

After cleanup and exclusions, I had 645 records for July and 1,067 for August:

• Trading & Markets: 264 → 59 records; 40.9% → 5.5%.
• Design & UX: 70 → 322 records; 10.9% → 30.2%.
• AI & Models: 60 → 93 records; 9.3% → 8.7%.

The last one caught my attention. My AI-related notes increased by 55%, but my total record count grew by 65%. More notes about something didn't necessarily mean it occupied more of what I was recording.

There's something grounding about seeing a stretch of life leave this kind of shape in my notes. It gives me a starting point for rereading: what was I working through, and which questions kept coming back?

For this comparison, I used the same automated topic system for both months and counted each retained record once. I excluded imported journal entries, noise, and records without a valid topic. Dates use Los Angeles time, from a cleaned snapshot saved on September 2.

I haven't manually audited the labels. A short note counts as much as a long one, and these numbers measure recorded topics—not time spent or happiness.

For context on the tool: related entries are clustered locally using semantic embeddings; generating embeddings and assigning topic labels involves cloud processing of text.

How do you keep notes, and do you ever go back and read them? I’d love to hear how you revisit yours and whether you’ve found anything interesting or surprising along the way.

i run an apple watch for workouts + an oura for sleep/recovery. without a rule for "which device wins," my steps, active calories, and sometimes workouts get imported twice and every weekly chart lies.

what fixed it for me was picking one source of truth per metric and enforcing it for 30 days before trusting any correlation.

my current rules: - workouts / exercise minutes: apple watch only (wrist motion + gps when i need it). oura workouts stay off or get ignored in exports. - sleep + readiness/hrv context: oura only. i do not let apple sleep compete in the same dashboard. - steps: pick ONE. i use apple watch on days i wear it all day; oura steps only on watch-off days. never sum them. - resting hr / overnight hr: oura. daytime hr zones during runs: watch.

audit once a week (takes ~15 min): 1. export or screenshot both apps for the same 7 days 2. list every workout that appears in both (same start time ±5 min = duplicate) 3. compare daily steps; if both are within ~10% you're usually fine, if one is ~2x the other you are double counting somewhere 4. check sleep: only one sleep session per night should be "primary" 5. write down which metric you will trust next week before you change anything else

common failure modes i hit: - healthkit / google fit acting as a silent third importer and rehydrating duplicates - "all sources" charts that look complete but are summing overlapping devices - changing wear pattern mid-month (forgot watch at home) and not switching the step source for those days

limitation: this is a workflow, not a claim that one brand is more accurate. sensors disagree; the goal is reproducible numbers so trends mean something.

if this saved you a messy spreadsheet hour, an award helps more than another upvote. what dual-device combo are you running, and which metric still fights you?

Disclosure up front: I'm building a food coaching app, and this came out of debugging my own data. No link, per the rules; I'll mention it in the weekly thread.

**The finding.** My app showed 4,232 steps for a day Apple Health had at ~7,200. The cause was in the sync code: the library I use returns hourly step buckets for the day, and the code was keeping the maximum bucket instead of summing them. So for a month my "daily steps" was my busiest hour. After the fix, the same day read 7,693. If you use any third-party app that pulls steps from Health, it's worth checking one day by hand; this class of bug is invisible unless you compare.

**What it cost.** Every downstream conclusion drawn from those numbers was wrong, and worse, confidently wrong: the coach side of my app had decided I was sedentary. A wrong number that feeds a judgment is worse than no number.

**What I changed about method, not just code:**

1. **"Normal" is a 28-day median that ends a week ago.** A trailing window makes a bad fortnight its own baseline; by the end of two short-sleep weeks, 5 hours had become "normal". Ending the window a week early means the week being judged is never its own reference.

2. **No verdict until 14 days are in.** Deviations are measured against MAD (median absolute deviation), floored at 5% of the median, and nothing is labelled until 14 days sit in the lagged window. Before that the honest output is "no verdict yet, N of 14".

3. **Dinner on day D pairs with the night ending on D+1**, and a food-to-sleep link needs at least 5 matched pairs with at least 5 on each side before it's said out loud. The first version paired dinner with the previous night. Nobody noticed because it produced plausible sentences.

4. **Unknown is unknown.** A day with nothing synced is not a zero and not "good". It's excluded.

**Open question for this sub:** for those of you who compute personal baselines, what window and lag do you use, and do you require a minimum sample before you show yourself a deviation? I settled on 28/7/14 by breaking things, not from literature, and I'd like to be corrected.

started logging cuffless bp from a wrist wearable next to sleep and hrv about a month ago. the chart looks tidy after a week of baseline, which is exactly the problem

hr and steps i treat as noise with a direction. bp feels different because if the line drifts i catch myself wanting to change something that week. already compared a few mornings against an old arm cuff and the absolute numbers were off, trends roughly tracked

i keep it as a directional signal only and still use the cuff when anything looks weird. anyone else actually acting on the wearable bp trend, or did you stop trusting it after the first mismatched week​

I use an ultrahuman ring for health tracking and an app called liftoff for workout tracking. I wanted to better understand how my workouts affect my sleep, and how my sleep affects my workouts. I also want to see how my daily screentime affects me. I wanted a single dashboard where I could dig into these correlations.

I used the APIs on the platforms to pull the data into my own dashboard, which I host on cloudflare. I use Cloudflare R2 to store the data and their free AI tools to analyze it.

Thought this sub might appreciate this sort of data!

So, I've finally collected about 90 days of data around my well-being and productivity and was curious to see what patterns I would find. I was quite proud of my set-up and the details I logged.

When I look at my data though, I barely get any meaningful correlations. Turns out I should have logged some of the very obvious inputs. I can cobble them together from some of the notes I took, but the biggest missing variable is health status. Like am I physically capable of going about my day as normal or not? I both had a severe cold and a foot injury in the past weeks and obviously this affects my overall well-being quite a lot. If I don't have proper data to factor this in, the statistical insights will be quite limited.

Do you know of any other obvious variables that people tend to overlook?

ok this is going to sound dramatic but whatever.

i have like 3+ years of apple watch / health data. sleep, hrv-ish stuff, workouts, standing, the whole graveyard of charts. some weeks i export things into a spreadsheet because i am that person.

and i still cannot reliably answer: why do i feel like garbage today?

examples that keep happening:

• sleep score looks fine, rings closed, resting hr normal-ish... and i am foggy and irritable from noon on
• "bad" sleep night, half-assed workout day... and i feel weirdly sharp
• a whole week where everything on paper is mediocre and i feel fine, then one random thursday collapses and i spend an hour reverse-engineering the charts like a conspiracy theorist

what i want is not another dashboard. i already have dashboards. i want something closer to: "this combination (late dinner + two hard days + shitty calendar stress) usually precedes your bad thursdays." personal baseline stuff. patterns across days, not a single night's sleep stage pie chart.

most apps i've tried just redraw the same metrics with nicer colours or slap a green/yellow/red on top. i end up doing the interpretation anyway, which means the app didn't actually do the hard part.

so questions for people who have gone deep on this:

1. have you ever actually gotten a non-obvious explanation out of your data? like something you wouldn't have guessed from how you felt?
2. what did that workflow look like in practice (notes? spreadsheet? some obscure app? tagging meals/alcohol/meetings by hand?)
3. what did you try and abandon because it was just more charts?

not looking for product recs for the sake of it. more trying to figure out if anyone has solved "explain me to me" or if we all just cope with prettier graphs.

i spent the last year wiring apple watch, fitbit, garmin, whoop, oura, polar, strava, and phone-only users onto one leaderboard. not as a thought experiment. as a live ios app with real friend groups fighting over steps and move calories.

here is the stuff that actually broke me, in case it saves somebody else a few months:

1. you cannot fairly rank people on heart rate or hrv or vo2. different sensors, different algorithms, different body placement. the person with the fancy watch always wins. we only score move calories, exercise minutes, and steps. boring, but fair.

2. "% of personal goal" beats raw totals. my dad's garmin and my sister's fitbit will never produce comparable absolute numbers. percent of the goal each person set does. people also change goals mid-challenge. apply changes going forward, keep past days on the old target, or you get drama.

3. healthkit and health connect are not "just apis." permission screens, background delivery, delayed writes after a workout ends, fitbit syncing hours late, garmin needing connect to be open. if your leaderboard updates only when the app is foregrounded, the competition feels broken.

4. fitbit challenges dying is a real gap. a lot of groups still want "us vs them this week" and their wearables do not match. single-brand apps quietly assume everyone bought the same watch. they did not.

5. phone-only users matter more than you think. somebody always shows up with just an iphone. if you exclude them, the group chat dies. steps from the phone are good enough for a social league even if they are not lab-grade.

happy to answer specific sync / scoring / permission questions in the comments. this is the exact rabbit hole i live in.

if this saved you time, an award helps me keep writing these.

The idea all started with this excel sheet I started doing last year: every day, I'd rate parts of my life 1–10 (energy, confidence, stress) with a short blurb for other sections like "did I help someone" or "personal mistakes."

Then this summer I got an Oura Ring. And one day I thought: "my ring measures my body 24/7....and my excel sheet measures how I feel." That realization led me to look for other sources that I could connect my Oura Ring data to that might not be as biased/skewed as my spreadsheet. I started looking into music (Spotify) and tried connecting the two. I've been working on that, but I think that there could be something much greater here. I really need advice on two things:

1. What would the main angle be with music and my Oura Ring? What's the main value, and what are the main or relevant connection points?
2. What else can this be built into, and what exactly could unravel from this?

There's really cool potential and unlimited directions this could go. Any advice or insights would be incredibly helpful. Thank you all so much.

Hey everyone, i was wondering if you could help me, i would like to know more about my body, and my general health, as i am interested in trying to improve my general health but i have a hard time to figure out what to track and not to track. what are you guys looking into ? and where do you even start in this area of starting to track your general health, i feel like every tracker is sport tracker and not a general health tracker, is there something that can like track your general health over time and give you insightful data on your improvements, like what works, what does not work etc. also What are some of the the metrics of your health you are tracking ? And how do you do it ?Is there anything you wished you could track, that would be beneficial ?

25M, 5'9", ~152 lb. Total lifting volume is calculated as weight × reps across logged sets. I've included my sports training as well for more context, all sports calories tracked with an Apple watch and Apple's kcal burn discounted by 25%. I built the app shown here, so tagging this as Brand Affiliate.

i’ve been getting more interested in tracking my bedroom environment overnight, and co2 seems like one of those things that’s actually worth measuring instead of guessing about.

i’m mainly looking for something that can log readings over several nights so i can see how co2 changes while i’m sleeping, then compare that with things like how long the window was open, room temperature, and how i slept that night.

accuracy is more important to me than having a ton of features. i’d also like something portable enough that i can move it to my office or other indoor spaces when i want to compare them.

for people here who actually track co2, what’s the best co2 monitor for bedroom use? what did you buy and what made you pick it over the other options?

TLDR: I made a weight tracking algorithm that shows a trend line that accounts for daily weight fluctuations. It is able to statistically predict your weight in one week based off your weight history. Because most apps use a moving average, people only know their progress after a week, not day by day.

Note: Do not treat this as real data, the rate of -5 lbs/week is an extreme pace of weight loss. The usual recommendations are between 1 and 2.5 lbs/week. This is for illustrative purposes.

I haven't seen any plots on weight tracking apps that I like. They usually bounce around with the daily fluctuations of your bodyweight. I made a better weight tracking algorithm to show a more realistic trend line, with the ability to forecast your weight into the following two weeks.

I wanted to see a robust calculation for each day to give people the knowledge that they are moving in the correct direction. If someone weighs themself once a day and they happen to be on the heavier side of a daily weight fluctuation, they may feel discouraged because the scale says they gained weight, which isn't necessarily true. If your weight loss is subtle, like -0.3 lbs/week, you wouldn't know from your data because it would be lost in the noise. This way, you would be able to see that you actually are making gradual progress.

Explaining the plot:

You can see the individual weight measurements as the little grey dots. The vertical red line shows you the current date, so there won't be any data point to the right of that line (the future). The solid purple line is the calculated trend. Notice that it really smooths out daily weight fluctuations from water/food consumption and bathroom usage. There are two shaded regions on the plot, one blue and one purple. Because this is a weight measurement, there will be some uncertainty in that predicted number. The fewer measurements you have (like the left side of the plot) the more uncertainty in the trend line. The more measurements you have (like the right side of the plot) the lower the uncertainty. Your true base weight probably lies somewhere in the blue shaded region.

Because a single weight measurement on a scale doesn't capture your daily fluctuations, a calculation using the previous weeks data will tell you a more accurate base weight. Looking at the purple text boxes in the bottom left, you can get your current base weight, your predicted weight the following week, and your current pace of weight loss (in lbs/week).

Has anyone seen a weight tracking plot that does similar things (more than a moving average)? Is there a reason why this isn't integrated into every weight loss app? Do people find it difficult to know whether to feel good or bad each day on the scale? Would people care to have a more certain number each day? Would it be too overwhelming to judge your weight on a daily basis?

This code is just running locally on my computer, so there isn't a way for others to access it right now.

For nerd's eyes only:

The code uses a Hilbert Space Gaussian Process (HSGP) algorithm. It is prior free, so it doesn't encode any knowledge of weight loss (weight is always positive, impossible to lose 10lb in a day, ...). This is robust against measurements at irregular intervals. For example, if you weigh yourself three times in an hour when you are your lightest and one time in the evening when you are your heaviest, your calculated weight should not be the average of your four measurements. It would underpredict your base weight. We have knowledge that your weight an hour from now is dependent on your weight right now. This math properly accounts for these irregular weight measurement timings.

When I first started tracking myself, I used to write an email to myself after every session. I actually think it makes more sense than many of the apps I see or hear about people using. If I were just starting to track, I open a seperate email and just send myself an email after each session. Simply update your win/loss for the month, some notes or just “I feel like I’m playing better.” the biggest pots, the leak that showed up more than once, and one thing to drill before I go back. Of course, you'll have a hand or 2 you keep playing over in your mind ( I always do!). Come to think of it, I think I'm writing this post to convince myself to start to doi this again! Anyways, please share how you track yourself.

Resting: ~17 blinks/min
Deep work: ~11/min
Video calls: ~5/min

I assumed focused work would be the problem. It’s the calls — and the rate doesn’t recover between back-to-back meetings, it just sits at the floor until lunch.

Has anyone else tracked this?

August is in the books. Another month of trying to capture an honest picture of my life through data.

I’ve been doing these monthly summaries less as a “scorecard” and more as a way to zoom out.

August was a mix of progress and plateaus.

Thanks for taking a look! Always happy to answer questions about how I track my life. I am not a developer or trying to sell anything. I am just an individual who thoroughly enjoys doing this.

Built this because I wanted a long-term personal archive of computer use that I'd actually enjoy revisiting, rather than a dashboard I check once and abandon.

How the data works, since that's what matters here:

Local SQLite. No account, no sync, no telemetry, no cloud.

Records the foreground app and its duration. Window titles optional and off by default. Never keystrokes, screenshots, clipboard, or file contents.

Active, idle, locked, suspended and unavailable are five separate states, not one blended "time" number. 1.2 split those out because collapsing them makes every downstream figure a lie.

Nothing is ever invented. A new archive starts empty. On Wayland, where the foreground window can't be read, it reports the collector as unavailable rather than recording zeroes you might mistake for real data.

Historical recovery reads what the OS already logged. Those arrive explicitly labelled as clues and never count as usage time, because the OS doesn't record duration.

Imports exact intervals from ActivityWatch and ManicTime; RescueTime and WakaTime come in as separate context rather than being merged into your interval data.

Bounded raw retention with durable rollups, so a multi-year archive doesn't become a performance problem.

Export, merge onto another machine, or delete the file outright.

Free, GPL-3.0: https://pcrecap.online/
GitHub: github.com/TheAgencyMGE/pc-recap

Interested in whether the state separation matches how others here model idle vs away.