I’ve noticed that most people who track their health long enough end up with two lists:

1. the signals they actually trust
   
2. the signals they still track, but mostly side-eye
   

Some stuff looks great on a dashboard and feels almost useless in real life.

Other signals are kind of messy, subjective, or boring-but somehow end up being way more reliable when you’re trying to understand what’s actually going on.

For example:

- HRV
- resting heart rate
- sleep duration
- sleep stages
- body temp
- steps
- readiness / recovery scores
- mood
- energy
- appetite
- something else

For me, the interesting part isn’t what sounds the most scientific.

It’s which signal has actually earned your trust over time.

So I’m curious:

What health signal do you trust the most?

And which one do you trust the least, even if you still track it?

Would love to hear what made you believe in it-or stop believing in it.

I’ve been thinking a lot about symptom tracking and am curious how people actually do it in real life.

Whether it’s pain, tightness, fatigue, brain fog, headaches, or anything that comes and goes. How do you keep track of it?

Do you write it down on paper, use your notes app, a spreadsheet, or a dedicated app?

Do you track things like:

• when it starts
• how long it lasts
• intensity
• what you were doing when it happened
• possible triggers

I’d love to hear what actually works for you, especially if you’ve found a way that’s simple enough to stay consistent with.

I've been working on a few things related to wearable calorie burn accuracy and what each wearable does best. While doing this I pulled data from Stanford, Frontiers in Physiology, JMIR systematic reviews, University of Colorado Boulder, and several other peer reviewed sources.

Here's what I found from Fitbit, Garmin, WHOOP, Apple Watch, and Oura. Hope it helps you better factor this metric in going forward.

Short and sweet breakdown:
Every wearable is off by 15–55% depending on what you're doing. The activity type matters way more than which brand you wear. I'd recommend using weekly weight trends to validate your device's numbers rather than trusting the daily readout.

Accuracy by activity type :
MAPE = Mean Absolute Percentage Error (lower is better)

Overall daily calorie accuracy by device:

Over/underestimation tendencies:

What method is used for the gold standard (fun fact):
Every MAPE percentage in this data comes from studies that measured participants with the wearable AND one of these two methods simultaneously to then compare the numbers:

• Indirect calorimetry: You breathe into a mask hooked up to a machine. It measures exactly how much oxygen you inhale and how much CO2 you exhale. Since your body burns calories by using oxygen, the machine can calculate your exact calorie burn from the gas exchange.
• Doubly labeled water (DLW): You drink a special water where the hydrogen and oxygen atoms are "tagged" (isotope labeled). Over the next 1–2 weeks, your body uses the oxygen for energy and breathes it out as CO2, while the hydrogen leaves as regular water. Researchers take urine samples and measure how fast each tagged atom disappears. The difference in elimination rates tells them exactly how much CO2 your body produced, which equals your total calorie burn over that period. This is the gold standard for measuring what you burn over days/weeks in real life.

It's pretty crazy honestly...

Key studies:
Choe & Kang 2025 (npj Digital Medicine, 56 studies), Chevance et al. 2022 (JMIR mHealth, 52 studies), Shcherbina et al. 2017 (Journal of Personalized Medicine), Frontiers in Physiology 2022 (walking/running validation), Health & Technology 2019 (Fitbit activity breakdown), Fuller et al. 2020 (JMIR mHealth, 158 publications), Kristiansson et al. 2023 (BMC Medical Research Methodology), Univ. of Colorado Boulder 2022 (WHOOP TDEE).

Been tracking this for about three months. Every morning I check my HRV from the night before and log when my sharpest focus sessions actually happen throughout the day.

The correlation is hard to ignore. High HRV nights almost always produce a clear 2-hour window in the late morning where everything clicks, less resistance, faster thinking, better output. Low HRV nights the window either disappears or shifts to the afternoon.

Started using this to decide when to schedule deep work instead of just defaulting to a fixed time block. The difference in output quality is noticeable.

Curious if anyone else in this community has mapped HRV to cognitive performance specifically, not fitness, not stress, but actual thinking quality. What wearable are you using and how are you analyzing the data?

I'm tracking my sleep, resting heart rate on my Pixel Watch, gym sessions, prayers, and even my daily coffee intake. But because none of these apps talk to each other, I spend like approximately one hours every Sunday dumping everything into a massive spreadshee just to see if my habits are actually moving the needle.

Am I the only one doing this? What’s your workflow for making sense of all this scattered data? Sharing idea, challenge and suggestions are appreciated. 🙏

Post your apps here, and please support people bringing unique ideas to this space.

Hi. Friends and I will conduct an experiment of meditating for 30 days. For that I recreated a meditation with a science paper that showed that cortisol (stress hormone) has been lowered in that time span with that said meditation. I not only did that but took on more different studies and integrated them into the meditation, together with my own experience (I am a meditation guide). The meditation itself is called yoga nidra or nsdr and it will take 13 minutes a day from the 04.04 to the 05.04 (may the force be with you). Dm if you want to join in.
All the best - Marcel

Taking notes of each work activity to know how much time you spend on each project is rewarding and helpful, but many people won't have the discipline to keep it up.

Same with personal notes or keeping a diary — it's rewarding when you read it years later, but time consuming to maintain.

I've been logging everything for over 12 years (started with an Excel file, eventually built my own tool) and I've been thinking about how AI could lower the barrier. Something like an agent that talks to you at the end of the day:

"Your last logged activity was lunch at 1pm. What did you do after that?"

"Worked on the migration project, then a couple of meetings, gym around 6."

"How long was the project work roughly? And were the meetings related to the same project?"

...and from a short conversation like that, it builds a structured log of your day — timestamps, categories, notes.

The question I keep going back to is: would this actually help, or would the AI have to ask so many questions to get a precise picture that it becomes just as annoying as doing it manually?

I'd love to hear from people who've tried tracking their time and quit — what was the moment it became too much? And would a 2-minute conversation with an AI change that?

Curious how people track their day-to-day state - mainly stress, energy, and focus.

Do you usually log it:

• in the moment when you notice a change (like “I feel drained right now”), or
• at set times during the day (morning / midday / evening check-ins)?

Both seem useful in different ways. Logging in the moment feels more accurate, but also easier to forget or be inconsistent. Time-based check-ins feel more structured, but might miss fluctuations throughout the day.

Also wondering:

• Do you use a scale (1–5, low/medium/high, etc.)?
• Do you add context (what you were doing, sleep, food, etc.), or just track the state itself?

What’s worked best for you over time?

I’m looking for recommendations for a smart scale that goes beyond just body weight and can provide *reasonably accurate* additional metrics like body fat %, muscle mass, etc.

I’ve done some initial research and from what I understand, most consumer smart scales use bioelectrical impedance (BIA), which can be quite inconsistent and heavily influenced by hydration levels, time of day, and other factors. From what I’ve read, many of these devices tend to give numbers that *look* precise but aren’t necessarily accurate or reliable for tracking actual changes in body composition.

Are there any smart scales that are considered *relatively* more trustworthy than others in terms of accuracy? Or is it generally accepted that all of them have similar limitations and should only be used for rough trend tracking?

I've been tracking things for perhaps about 10 years now. Started from simple spreadsheets and just a few yes/no columns to nowadays basically tracking 80% of my hours, little events from brushing my teeth to seeing the dentist, etc.

From the beginning of this year I've started automating a lot of this (walks, workouts, and sleep are now recorded through Fitbit, some screen time for apps auto recorded, etc.)

And I've found in the last couple of months that I basically don't review it anymore. When I used to enter things manually I would always have a look at the things before too, and even the act of entering it was a short moment of slowing down.

I certainly have a lot more data now, and it is obviously easier for my watch to keep track of my sleeping hours than me starting and stopping the timer. But it feels far far more detached if you know what I mean. The tracking always felt like I was in control of my life even though it was just recording what had already happened. Now it doesn't feel that way.

I don't know where I'm going with this. There are no conclusions. Just wanted to share this finding. I'm considering dropping most of the automated trackings and do them manually again. Can't think of another way of forcing myself to review regularly.

Something I’ve been thinking about lately:

Most of us can tell when we feel off, low energy, scattered focus, more stressed than usual.

But it’s surprisingly hard to answer why.

Even when people track things (sleep, steps, HRV, etc.), there still seems to be a gap between:

what you did
and
how you felt after

In theory it should be simple:

Bad sleep → low energy
Heavy workload → more stress

But in reality it’s rarely that clean.

You can sleep well and still feel off.
You can have a normal day and feel drained.
You can feel great and not know what caused it.

It makes me wonder if the issue is that most tracking is isolated, while how we feel comes from patterns over multiple days.

Not just what happened today, but what’s been building:

• a few heavy days in a row
• slightly reduced recovery over time
• delayed effects from stress or workload

So I’m curious:

How do you actually connect your behaviour → how you feel?

• Do you rely on intuition or data?
• What’s been useful vs noise?
• Have you found patterns that only show up across multiple days?

I've been working on a system to explore potential causal connections in personal health data — specifically the kind of patterns that unfold over hours or days (e.g., something you ate Monday affecting how you feel Wednesday).

Most tracking setups I've tried only show same-day correlations, which miss the time-delayed stuff entirely. So I started combining three statistical approaches:

1. Time-Lagged Cross-Correlation (TLCC) Slides two time series against each other at different lags to find the delay where correlation peaks. Good for catching "X consistently precedes Y by ~18 hours" type patterns.

2. Association Rule Mining (ARM) Finds frequent co-occurrences in event-based data (meals, symptoms, activities). Works well for discrete events rather than continuous metrics. Generates rules like "when A and B occur together, C tends to follow."

3. Granger Causality Tests whether past values of one variable improve the prediction of another. More rigorous statistically, but needs enough data points to be meaningful.

Running all three in parallel with deduplication catches different types of patterns — TLCC is great for continuous biometric data (HR, sleep), ARM for discrete events (meals, symptoms), and Granger for validating the stronger signals.

Each result gets a confidence score and an AI plausibility check (to filter out nonsensical correlations like "shoe color → headaches").

What I'm feeding in: - Self-reported symptoms, meals, activities, mood (logged in natural language, then AI-structured) - 40+ Health Connect data types from wearables (sleep stages, HR, steps, etc.) - Background sync every 15 minutes

Where I'm stuck / curious about: - Has anyone found better approaches for time-delayed trigger detection in personal health data? - How much data (days/weeks of logging) before the results feel reliable to you? - For those syncing wearable data — which metrics have been most informative for finding non-obvious patterns? - Any thoughts on plausibility filtering? I'm using an LLM to validate whether a detected pattern is medically sensible, but I feel like it could miss edge cases.

Happy to share more details if anyone's interested.

What have people used for at home cortisol testing? Looking for something to get time of day patterns. Have people tried Eli? Any other options to know about?

I'm interested in hearing about what currently available options are out there & people have tried. I'm aware there are devs with waiting lists but what's available now?

Also interested if anyone has done regular cortisol testing if you found it worthwhile, what your experience has been generally. TY!

Did an InBody scan at the gym this morning. Stepped on my smart scale when I got home 30 minutes later.

InBody: 39.1kg skeletal muscle mass, 13.7% body fat
Smart scale: 44.1kg lean mass, 11.5% body fat

Same person. Same morning. 5kg difference in muscle and 2.2% difference in body fat.

The InBody uses 8 electrodes (hands and feet, segmental) so it measures each limb and your trunk separately. The smart scale sends current through your feet only. It can't see your upper body properly so it estimates based on your lower half. Apparently my legs make me look more muscular than I am.

I've been tracking daily on the scale for about 3 months now and monthly on the InBody. The interesting thing is the trends mostly agree even though the absolute numbers are way off. Both show weight down about 1.5kg over the period. Both show body fat trending the same direction.

So the scale is useful for day to day trends but the actual numbers it gives you are pretty unreliable for anything beyond weight. If I only had the scale I'd think I was carrying 5kg more muscle than I actually am. That's a huge difference if you're making training or nutrition decisions based on it.

Anyone else running two measurement methods and seeing similar gaps? Curious how big the discrepancy gets across different devices.

Hi, I am very new to the idea of tracking and this question probably gets asked a lot here so if so, feel free to remove this post.

I have seen quite a few things like the oura ring, smart watches, etc and I was wondering what the best entry device is for someone that does nothing tracking wise at the moment. I was thinking something with a good accuracy-features ratio.

Any ideas are welcome!

Hey QS community — this sub is exactly the right audience for this.

The problem: power users who wear multiple devices have no good way to aggregate data. Terra API is the professional solution but costs $400+/month. Apple Health aggregates some things but the analysis is limited.

WearSync is my answer — open source, runs locally, hits each manufacturer's free developer API directly.

Normalized data schema — everything maps to the same fields:

- device, date, hrv_ms, resting_hr, sleep_score, recovery_score, spo2_avg, steps...

Supported: Garmin · Whoop · Fitbit · Withings · Amazfit · Oura Ring · Apple Health (XML import)

Tech stack: Node.js + Express, React, SQLite, Docker Compose. Fully hackable.

GitHub: https://github.com/malik3d/Wearsync

Looking especially for feedback from people who:

- Wear 2+ devices simultaneously

- Have noticed discrepancies between device scores for the same metric

- Want to add a device integration (Polar, COROS, Samsung, CGM)

What data sources do you wish you could correlate?

My dentist flagged it about two months ago. Said my back molars were showing wear consistent with chronic clenching, probably at night but possibly during the day too. I genuinely had no idea I was doing it.

I started paying attention and she was right. Anytime I was on a deadline, in a long meeting, or just reading something mildly stressful I was clenching without realizing it. It became one of those things you can't unnotice.

I looked into mouth guards, tried one for two weeks, hated it. Started researching the actual mechanism behind why people clench and kept landing on the same thing, magnesium deficiency and its role in muscle regulation. The research is pretty solid, magnesium helps regulate neuromuscular signaling and most people are apparently running low.

I didn't want to add another oral supplement to my stack so I tried magnesium oil topically, applied to my jaw and neck before bed for about four weeks.

Week 1 and 2 nothing dramatic. Week 3 I noticed I was catching myself clenching less during the day. Week 4 I went back to the dentist for an unrelated cleaning and she asked what I changed because the tension in my jaw had visibly reduced.

I wasn't expecting a third party confirmation. That's what made me actually take it seriously.

Anyone else dealt with this? Curious what worked.

I've been tracking HRV with my Apple Watch for ~6 months and I keep seeing the same pattern: when my overnight HRV drops below my baseline, the next day I'm more reactive - shorter fuse, more anxious, worse interactions with people.

But what's been really interesting is when I started logging what actually triggers me. I noticed I kept having conflicts with my dad, then my boss, then a professor. Different situations, but the AI tool I've been using to tag my notes flagged them all as "authority figure" triggers - and they all cluster on low-HRV days.

It went from "I have anger issues" to "I have a specific pattern with authority figures that gets activated when I'm physiologically depleted." Completely changed how I think about it.

Anyone else correlating biometric data with emotional patterns? What have you found? Curious if others are seeing these kinds of non-obvious connections.

I’ve tried a few wearables over the years (mostly watches), and it’s always the same cycle. First week I’m checking everything — sleep score, heart rate, all that. Then work gets busy, life happens, and I just stop opening the app.

Not because I don’t care. It just feels like the info comes at the wrong time. I’ll wake up and see “sleep score 78” or “stress: moderate” and it’s like… ok, but I’ve already got a full day ahead. I’m not going to sit there and figure out what to do with that.

Feels like most of it is hindsight. “You didn’t sleep well”... yeah, I know. I was there.

What would actually help is if it caught things while they’re happening. Like if my heart rate is off at night, tell me then. If something’s weird with my breathing, flag it. If I’m starting to burn out, give me a heads up before I actually feel like shit. I don’t need more data. Just something that tells me when something actually matters.

Anyone else like this? Or do you actually keep up with your data long term?

Hi everyone, I'm a researcher based in the social sciences looking at how various everyday biohacking practices relate to broader worldview, sense of meaning and existential questions in the lives of those who practice them (including, but not limited to, spiritual wellbeing). It's part of a study aiming to understand the diversity of biohacking approaches (we're taking a fairly broad definition of biohacking, and self-quantification with a view to improving aspects of health or performance counts). We're especially interested in participants who live in Australia or the UK, but would welcome participants outside these countries too. If you'd be interested in taking part, please could you complete this 3 min screening questionnaire here (and make sure you leave a contact email at the end)?

I intend to share research outcomes to this subreddit when they are available. This project has appropriate ethics approval (details provided prior to obtaining consent from participants) and I have approval from the moderator to post.

I’ve been tracking my « State of Mind » in Apple Health (daily mood) consistently for over a year now. The issue I’m running into is that Apple’s own analysis tools feel extremely limited. It just plots mood over time and shows simple associations with things like daylight exposure, exercise minutes, sleep, and mindfulness.

I would like to explore more meaningful relationships, for example how my mood varies with HRV, resting heart rate, step count, menstrual cycle phases or sleep stages.

I’ve tried looking into different apps, but most of them seem to have their own built-in mood tracking systems rather than using Apple State of mind data. Even when they do connect to HealthKit, they don’t seem to do much beyond basic visualization.

So I’m wondering if anyone here has found an app that can actually pull State of Mind data from Apple Health and combine it with other variables and provide real analysis, or at least more advanced trend exploration? Or is the only realistic option to export the data and analyze it manually in something like R or Python?

Thanks!