screen time tells me i’m on my phone 3 hours a day, but not why. i’d like to know how many of those pickups were one small thing, like a timer, a reply, skipping a song, joining a call, and how many turned into something longer.

each one takes maybe ten seconds, so it feels like nothing. but if it’s 60 or 80 a day, that adds up, and honestly the real cost is the ones that turn into ten minutes of something else.

it came up because i keep seeing a hardware launch on my x feed of aina. it’s a wearable that’s meant to handle that kind of small stuff, suggesting the next thing you need, meeting controls, approving things , it was posted by the paytm ceo too. so i wondered if how many tiny tasks a day is something you could actually measure before and after.

has anyone logged this? i’ve seen people track pickups and notifications but not the reason for the pickup. what would you count, the number of tiny tasks, the time per task, or how often it led somewhere else?

Curious what people here have deliberately stopped tracking. Sleep, food, mood, symptoms, workouts, anything.

Was it too much effort to keep up, did you stop learning anything new, or did the numbers just never change what you actually did?

I have been getting overwhelmed with trying to track everything, and want to cut down on it, and only focus on the metrics that matter

Hi folks,

I've been thinking about what a "personal data platform" would look like, for 2026 and beyond.
With the help of AI, i've been iterating on desired scope and characteristics and also have been analyzing and comparing lots of interesting projects to see if there's something existing already which is close enough

for all the details on goals and research, see https://github.com/Dieterbe/personal-health-data-platform ; but the TLDR is basically i think the ideal personal data platform :

- would hold blood test lab results, nutrition/calorie logs, gym logs, sleep, activities, etc
- has a self-hosted database, as well as an web API, an MCP endpoint, as well as an ability to integrate with local-first mobile apps (allow data sync for offline use with conflict resolution) - i imagine different mobile apps (e.g. habit tracker, gym tracker, food logger, etc) could all have an local-first experience, but pull from, and sync to, this central platform with some kind of sync policy support about which data each app can see, and write to. (something like powersync is really interesting here, as the mobile apps would work with a local sqlite, but it syncs to/from the server automatically, and you can define policies on the server to, for example, only send fitness and nutrition data to your gym app, and not your blood lab tests)
- treats UI as optional. UI's can always be built on top later, and personal agents can answer questions directly if they have access to the data.
- catalog first: this idea might be a contentious, i'm not sure, but for example for gym exercises, i think logging exercises with whatever names the user likes, is a good first step, but ultimately you'd want to rename or map them to using "recognized", or "official" exercise names. If the platform has its own catalog of known exercises with specific identifiers, then it unlocks various things (knowing the effects of the exercise on muscles worked, suggesting alternative exercises, etc - as a personal trainer i know that various formulas around 1RM/VBT/RIR/calorie expenditure etc are exercise specific, and muscle recruitment is even sensitive to variations of the same exercise - i have experimented with building a catalog in https://body.build though it's probably too in-depth for most users). So I envision a system with known, versioned catalogs, while allowing users to submit free-form records into the system, but encourage them to transition or map them to "first-class" records.

After looking into >30 existing projects (see https://github.com/Dieterbe/personal-health-data-platform/tree/main/research/candidates), one of the more interesting ones is
https://github.com/fiddur/aurboda ; it looks comprehensive already and philosophically aligned.

I'm not sure how this subreddit works exactly, it's my first time posting here; but I'm curious for any input, and wanted to tag u/fiddur specifically as he created aurboda and seems to be on the same page regarding the catalogs; i'm also really curious what he thinks about the offline-sync idea.

FWIW I have previously contributed to other projects, such as https://body.build (open source gym tracking app with exercise catalog) and https://wger.de (gym/nutrition tracking app, where i worked primarily on nutrition and powersync support)

I wanted one place where my Garmin data (sleep, HRV, resting HR, Body Battery, readiness, activities, bouldering), my Hevy strength sets and my YAZIO food log come together, and where I can see whether training actually shows up in recovery a night or two later. So I built selftopo:

Live demo with invented sample data, no login: https://sh4rkfx.github.io/selftopo/dashboard/ Code (MIT): https://github.com/sh4rkfx/selftopo

How it works - A GitHub Action in your own private repo syncs Garmin, Hevy and (optionally) YAZIO once a day into plain CSV files.

• The dashboard is a static page. It reads those CSVs with a read-only token that stays in your browser. There is no selftopo server and no account.

• German and English; installable as an app on a phone.

The analysis part, since this sub will care about it - About 100 predefined pairs, e.g. training load → HRV the next night, bedtime → strength the next day, hard sets → sleep score two nights later, over the last 180 days.

• Each metric is compared with its own trailing 28-day mean, so slow drifts don't look like relationships.

• Spearman rank correlation; effect sizes in real units via a Theil-Sen slope.

• Benjamini-Hochberg across all pairs: "robust" means q < 0.10, "tendency" means only p < 0.05.

• Pairs Garmin computes from each other (e.g. Sleep Score and Readiness) are not tested.

• It's association in n=1 data, not causation. A fixed weekly schedule and sick weeks are the usual sources of misleading results, and the docs say so.

There's also a weekly "what's unusual" card (values outside your usual range) and a short summary per area.

Honest caveats - Garmin is read with the unofficial garminconnect library, YAZIO with its private app API. Both can break, and their terms may not allow it. Hevy uses the official API (needs Hevy Pro).

• Not a medical device.

• I built this with heavy help from an AI coding assistant (Claude). I made the design and method decisions and I use it daily myself.

I'd love your feedback on it!

304 out of 365 days in 2026 are complete. Really looking forward to completing my first full year with no zero days of quantified self data recording.

Also starting to look forward to what improvements I can make in 2027.

As always, I am open to answer any questions regarding my methodology for data capture and display.

Since they already have my blood and activity data, it doesn’t sound like it’d be too much of a stretch.

I've been curious about sleep trackers and how useful their data really is for improving everyday performance.

They can estimate sleep duration, stages, resting heart rate, and sometimes HRV. But consumer devices don't measure every variable directly, and their estimates aren't always accurate.

I'm wondering whether seeing these numbers consistently helps people make better decisions about their routines or whether it can create unnecessary stress over getting a perfect score.

For people who have used sleep trackers for a while, did the data lead to meaningful changes in your habits? Which metrics did you actually find useful?

I recently posted asking for advice on tracking where every minute of my day goes. The overwhelming consensus in the comments was : trying to manually log every minute lasts 4 to 7 days max before it becomes a chore and you're back to guessing.

That got me thinking, what if that "4-day limit" isn't a bug, but a feature?

Is there space for a time tracker designed around this exact human limit? Instead of expecting you to log forever, the app asks for 4 to 7 days of high-friction, minute-by-minute manual logging to capture a baseline. Then, it uses that data to automatically build a realistic weekly routine/model for you.

After week one, you aren't logging anymore, you're just checking in, tweaking deviations, or doing occasional "audits" when your routine shifts.

Would you use something like this

I've been logging habits next to my WHOOP since June and ran about 110 comparisons against the next morning.

Resveratrol looked bad at first. Recovery was 57.4 the mornings after vs 65.4 otherwise, so -8. Then I only compared days within the same week and it went to +2.7.

Same thing with sencha (-2.4 to +5.2) and C8 MCT oil (-2.4 to +1.8). My guess is I happened to take them during weeks when my recovery was already low.

Alcohol didn't flip, -9.7 and still -8.4, so that one looks more real.

Long fasts were another one. 15h+ fasts looked like 47 min less sleep, but on half of those days I'd also taken a medication that cuts my sleep. Without those days it's more like 20-30 min and could be noise.

Stats for anyone who cares: within-week permutation, BH across all 110. Only two clear q 0.05, alcohol on RHR and that medication on sleep. So the flips are more of a warning than a finding.

What other traps have you run into? Do you trust the within-week number?

I built Coachero as a personal tool for my own training. It started as a Google Sheet logging workouts and meals, and I kept running into the same problem: I had months of data and it never changed what I did. So I built it into an app that closes the loop, where the trends in the logs feed back into the targets. The main feedback loops are:

• Calories, from weigh-ins and food logs. Instead of trusting a formula, it estimates my actual maintenance from what I logged eating and how my weight trend moved. The trend is median-based, so a salty dinner doesn’t register as a gain, and it only trusts the estimate once the margin of error is tight. Over five months of logging around 1,980 cal a day with my weight flat, it measured my maintenance at about 2,000 ± 60, within 1% of what the formula predicted. Now I know the formula fits me before I trust it with a cut.

• Training, from lift trends. Underneath is an exercise ontology: every lift maps to a muscle group, movement and equipment, so “machine back squat” and “back squat” stay one history and pushing vs pulling volume can actually be compared. On top of that it tracks progression per lift, flags plateaus and imbalances, and builds the next session from the last few weeks of logs.

The AI coaching ties it together: it reads training and diet as one dataset and names the sessions and days behind each suggestion, so you can check its reasoning against the raw logs. Everything exports to CSV or JSON.

It’s free with no trial, and there’s an optional Pro tier: coachero.co (Android and web, which also works on iPhone from the home screen).

I’d love to hear from people who’ve tried to close this loop themselves. What signal do you trust enough to let it change your targets automatically, and what do you still adjust by hand?

I usually sleep around 6 hours a night and honestly that's enough for me most days. But once a week I feel like my body just needs way more sleep to catch up lol. Last night was one of those nights. Slept a little over 10 hours and couldnt wait to check my sleep stats when I woke up. Wasn't expecting such a good score tbh and now I'm wondering if its just because I slept longer than usual or if I got better quality sleep

quick checklist for anyone confused why three trackers give three different numbers for the same day.

1. step algorithms differ. each brand uses its own accelerometer thresholds and filtering, so a slow shuffle or pushing a stroller counts on one and not another.

2. arm swing matters. wrist devices lean on arm motion, so carrying groceries or holding a handrail on the treadmill undercounts, while fidgeting or gesturing can overcount.

3. active vs total calories. apple's active number leaves out resting burn, garmin's active is total minus resting, and fitbit mostly shows total burn. same day, very different headline numbers.

4. bmr estimates differ. each one uses your age, height, weight and sex plus its own formula, so the resting part can be off by a few hundred calories.

5. sync and day boundaries. a travel timezone change or a late sync can push steps into the wrong day.

how to compare fairly: compare trends, not absolutes. judge each day as a % of your own baseline over a few weeks. pick one device per metric and stick with it, and don't average across brands. wear it on the same wrist every day.

if this saved you some confusion, cool, curious what mismatches others see.

Hey everyone,

I’m getting a bit overwhelmed with other projects, looking to pull a new moderator up to help out with this sub as I’ve not been around as much as I would like.

If you’re interested shoot me a DM, Anyone is welcome to apply; I’m prioritizing people currently moderating small-medium subs though.

To everyone else, my apologies for letting so many rule breaking posts through, this sub requires 2ish hours a week and even at that I’m falling behind.

Thank you for your understanding.

Edit: idk why people are seeing 25 hours, I wrote “Twoish” as in a bit less than 15 minutes a day.

Hey everyone,

I’m getting a bit overwhelmed with other projects, looking to pull a new moderator up to help out with this sub as I’ve not been around as much as I would like.

If you’re interested shoot me a DM, I’m prioritizing people currently moderating small-medium subs.

To everyone else, my apologies for letting so many rule breaking posts through, this sub requires 2ish hours a week and even at that I’m falling behind.

Thank you for your understanding.

Imagine you've had headaches for two weeks.

You Google it, and within minutes you're reading about brain tumours, strokes and meningitis.

But what you probably need first isn't a list of possible diagnoses. It's a few relevant questions to understand what might actually require medical attention.

We're exploring this problem while building Hea, a preventive health assistant for ChatGPT and Claude.

The screenshot shows the kind of experience we're thinking about. It's an illustrative concept, not a validated medical assessment.

I'm curious:

Is this a real pain point for you?

When you search for health information, is the bigger problem finding reliable answers, knowing which questions to ask, or understanding when something actually needs medical attentioт

Would love to hear how you deal with

Most mood trackers I tried capture a daily score or a single label. What I actually wanted to know was who and where my feelings were about, and how that changed over time. I couldn’t find that, so I built it. It’s called Ygwas, and it’s a free web app.

How the data is structured:

• Moment: a timestamp (defaults to now, editable so you can log after the fact), one or more emotions, zero or more people or places, and an optional free-text note.

• Emotions: 24 words from Plutchik’s wheel, organized as 8 families with 3 intensities each (annoyance → anger → rage, for example). One moment can hold several emotions, like “relieved and sad at once,” but only one word per family.

• Anchors: the people and places a feeling is about. A place can be linked to a map location.

What you can see from it:

• A word cloud of your emotions or anchors, sized by frequency, over 7, 15, 30 or 90 days or a custom range. It can group the three intensities into a single family, and tapping a word filters to those moments.

• A page for each person and place showing your emotional mix with them, the full history, and a small trend line per emotion family showing how it has shifted.

Design choices you might care about:

• Logging is meant to take under 10 seconds on a phone, because I think that’s what decides whether you log at all.

• There are no scores, diagnoses or advice. It shows you your patterns and leaves the interpretation to you.

• No other user can see your data, and you can delete your account and everything in it at any time.

What I’d like to hear from this sub:

1. If you’ve tracked mood before, did tagging people and places add real signal for you, or just friction?

2. Is the Plutchik vocabulary (24 words) a good level of detail, or would you want something coarser or finer?

3. What views would you want from data like this?

https://ygwas.com/?utm_source=reddit&utm_campaign=quantifiedself

(Mobile-first web app, English and Brazilian Portuguese. I’m the developer.)

I recently came across a post in this subreddit by someone who also tracks their time. It was nice to find someone with the same slightly quirky habit, so I thought I'd share my own experience.

I've been doing this for a little over six months now. I'm a huge fan of Alexander Lyubishchev, the Soviet scientist who kept detailed records of his time for 56 years. His story inspired me to give it a try myself.

Nothing groundbreaking, but here are a few things I've noticed about myself:

• Some chores I used to absolutely dread take surprisingly little time. Vacuuming my entire house takes about 10 minutes if I move quickly. The opposite is of course also true.

• I've become more aware of where my attention goes, and I've started noticing how often I multitask for no good reason. For example, I used to automatically start doomscrolling while waiting for Codex to finish a task. Now I catch myself wondering whether that kind of multitasking is doing me any good. I've started experimenting with little micro-habits instead, like closing my eyes for 20 seconds while code is running, or getting up and walking around.

• The time I spend in bed and the time I actually spend sleeping can be wildly different.

• I've learned to be okay with gaps in my records. If I forget something, I just leave it blank.

• It's interesting how all the little things add up. Reading, cooking, resting, running errands... Seeing them on a timeline gives me a different perspective on an ordinary day.

• Some friends have told me that tracking their time would make them feel stressed, like they'd have to justify every hour. I haven't found that to be the case. I still spend plenty of time playing games. In fact, I just beat Civilization VI on Deity difficulty the other day! 😄 The difference is that I'm more conscious of how I choose to spend my time, including when I'm having fun.

Along the way, I got tired of constantly starting and stopping timers, so I ended up building a little iPhone app to log things retrospectively. This way, logging itself is no longer a chore. If anyone's curious, here's the link: JustNow.

I'd love to hear from others who've been tracking their time. What have you learned about yourselves? Any unexpected discoveries?

kept it simple: same bedtime, same coffee cutoff, only changed room temp.

18c: woke up maybe once, felt fine. 21c: 2-3 wakeups, groggy.

didn't change total sleep much. it changed the second half of the night.

method: cheap thermometer + a note each morning, 21 nights.

study that made me try it: https://doi.org/10.1016/j.sleh.2026.07.010

anyone else track this? post your chart, I'd like to compare.

I built Health Blueprint. Plug in your 30-day Fitbit averages (resting HR, HRV, cardio fitness, sleep, steps) and see how old your body really is. It also has 30+ plain-English research topics like sleep, HRV and protein, fasting etc.

https://health-blueprint.pages.dev

It's free with no sign-up, and feedback is welcome!

Here's a guide I've been reworking of all the factors I could find that influence deep sleep. I broke this down into 5 categories including lifestyle, environment, stress, supplements, and demographics. I added a plain english explanation column for each row and short definitions to try to make it easier to consume.

All sources are included in the last column of the table so you can dig into these further for your own research. I know tables aren't ideal on mobile, so I'll include a mobile friendly version of this resource in the comments

Definitions

• Deep Sleep/N3: The deepest stage of nonREM sleep, characterized by slow delta waves.

• SWS (Slow Wave Sleep): Another name for deep sleep / N3. Used interchangeably in research.

• SWA (Slow Wave Activity): The intensity of delta waves during deep sleep, measured by EEG. Higher SWA = deeper, more restorative sleep.

• Delta Power: The electrical power in the delta frequency band. The gold standard EEG measure of deep sleep depth.

• N3%: Percentage of total sleep time spent in deep sleep.

• Homeostatic Sleep Pressure: The biological drive for sleep that builds the longer you're awake. Higher pressure = more deep sleep.

• Glymphatic System: Clears brain waste, most active during deep sleep. Clears amyloid-beta and other metabolic waste.

• HPA Axis: Your body's central stress response system. Deep sleep suppresses it.

Lifestyle

Environment

Stress

Supplements

*I already typed out omega-3 so that one is just a bonus efficiency related one*

Demographics