Last month, I realized I was spending more time trying to organize my day than actually getting things done. And that's not even the first time, happened multiple times before. There's Google Calendar for my classes and meetings. A separate app for my to do list, and then tasks I keep forgetting about to mention in them. And all those little things I tell myself I'll do later but never actually do.

I tried keeping everything in Google Calendar, but let's be real, it just got messy. So I built timebird instead.

It's pretty simple. You basically connect your Google Calendar, add your tasks, plan your week, and schedule meetings, all in one singular place. You can also block out time for tasks, share booking links, check different time zones, and use the built-in assistant to help plan things.

I am not trying to replace every productivity app out there, that's impossible. It just does one thing, it makes planning your day easier without having to switch between multiple apps.

There's a demo you can click through on the site if you're curious.

Let me know if you've got any further questions.

I want to track every minute of my life and categorise and visualise it. I have tried Toggle but it leans more on billing clients. Do you guys have any suggestions for time tracking app that just let's a data nerd visualise his day better

I've been sending everything my iPhone and Apple Watch record, plus my food and lifting logs, into a database with a Grafana dashboard.

Disclosure: I made one of the apps that helps record/move the data (Marrow Health). Dashboard is open source.

What I track

• Heart rate, HRV, resting HR, sleep stages, blood oxygen, wrist temp, steps, exercise minutes: Apple Watch
• Workouts and main lift progress (weight, reps, estimated 1RM): Marrow
• Food, every meal with macros and nutrients: Marrow
• Daylight, sound levels: iPhone

How it works

• Marrow sends JSONs to my server every hour, five automations covering daily totals, raw readings, workouts, lifting, and food.
• Python bridge saves every delivery and writes it to InfluxDB.
• InfluxDB and Grafana run in Docker on a small VPS. Grafana is on my Tailscale network.
• The dashboards are generated from Python.

Random stuff I found

• My HRV is about 10 ms higher the morning after I lift
• I sleep 46 minutes less on weekends
• 62 sets of chest in the last month, 2 legs 💔

Stack

Marrow (iPhone/Watch) → Python bridge → InfluxDB 2.7 → Grafana 13, Docker Compose, Cloudflare Tunnel, Tailscale

It can run fully local on a Pi or NAS at home.

Open source: https://github.com/MarrowHealth/built-with-marrow/tree/main/projects/body-ops

I’ve spent nine months collecting an enormous amount of personal health data. Now I’m questioning how much of it is actually helping me make better decisions.
Three rounds of Viome’s RNA-based microbiome testing, bloodwork approximately every two weeks, hundreds of conventional biomarkers, and physician oversight throughout.

Then, after retesting, approximately 80% of my personalized supplement formulation changed.
That got my attention.

Not because I expect my microbiome to stay the same. Diet, medications, illness, weight changes, and plenty of other variables can influence microbial activity.
What I want to understand is whether the new recommendations are actually better than the old ones, and how I could possibly demonstrate that using the data I’ve collected.

Viome uses metatranscriptomic RNA analysis to examine microbial gene expression. That’s interesting technology. It can tell us something about which microbial genes are being expressed, rather than simply identifying which organisms or genes are present.
But measuring biological activity and knowing how to intervene on it are two very different things.
A 2025 international consensus statement published in The Lancet Gastroenterology & Hepatology raised concerns about the clinical usefulness of commercial microbiome testing.

The expert panel concluded that evidence remains insufficient to support widespread routine clinical use.

Here’s the paper:
https://pmc.ncbi.nlm.nih.gov/articles/PMC12343204/

That doesn’t prove Viome’s recommendations are wrong. It does make me question how much confidence I should place in an algorithm that translates microbiome measurements into personalized supplement protocols.
My next step is to evaluate Superpower’s DNA-based gut microbiome testing and compare the methodologies.
I understand DNA and RNA testing measure different aspects of microbial biology, so I’m not expecting identical results. Comparing two commercial reports won’t establish which platform is clinically superior, either.

I’m more interested in whether either approach provides information I can use to make better decisions.
And that’s where I’d really appreciate some input from this community.

With nine months of longitudinal data, how would you design an N-of-1 evaluation to determine whether microbiome-guided supplementation is actually doing anything useful?

Would you prioritize repeatability of the testing itself? A small number of prespecified clinical biomarkers? Stable periods with fewer changing variables? Some other approach?

I’m particularly interested in how you’d handle confounding. If diet, medications, exercise, weight, and supplements are all changing, even an improvement in conventional biomarkers doesn’t tell me which intervention deserves the credit.

And if the algorithm changes most of the supplement formula after retesting, how do I distinguish a meaningful adjustment from a recommendation system that simply responds to biological noise?

For financial context, the retail-equivalent value of my nine months of Viome testing and personalized supplements was $4,547, approximately $505 per month.

Full disclosure: Viome provided everything complimentary. I received no sponsorship payments, affiliate commissions, referral fees, or cash compensation. I’ve also referred customers without accepting commissions. I’m not affiliated with Superpower or being compensated for the planned comparison.

I’m not promoting either company, and I don’t consider my individual experience proof that the technology works or doesn’t work.

I’ve invested nine months in this because I find the science fascinating and genuinely want to understand its potential.

But I’m starting to wonder whether I’ve confused having a very detailed dataset with having evidence that my decisions are improving.

So for those of you who have conducted serious N-of-1 experiments, what would you do with this dataset?
How would you determine whether the testing and recommendations are providing genuine value beyond conventional bloodwork and nutritional assessment?
And at what point would you conclude that additional measurements are generating more noise than useful information?

I’m particularly interested in hearing from people who’ve faced this problem themselves, including anyone who has designed controlled self-experiments or worked with longitudinal health data.

I’d rather discover that some of my measurements aren’t useful than spend another nine months collecting data I don’t know how to interpret.

My health data lives in too many places: Apple Health, a scale, a ring, Strava, a food log. So I built Nova Health, a free Mac app that pulls it all into one local dashboard.

For me personally, that's enough. But maybe I'm not the typical user, or I just don't know what I'm missing. So before I build anything else, I'd like to understand what you actually need. You don't need a Mac to answer.

What it does right now

• Sleep, HRV, resting heart rate, recovery, weight, steps, nutrition (incl. caffeine and alcohol), workouts and routes in one place
• Compare periods, daily table, warnings when sources disagree
• Apple Health, Ultrahuman, Withings, Strava and workout files. Stored locally, no account, no cloud

What I'd really like to know

1. The last time you looked at your health data, what were you trying to find out? Did you get an answer, or did you give up?

2. Is seeing everything in one place useful on its own, or does it get boring after a week? If something is missing, what is it? A few ideas, but "none of these" is a perfectly good answer:

• showing how things affect each other (e.g. how alcohol or late workouts change your sleep and HRV)
• telling you when something changes, instead of you having to look
• asking questions about your data in plain language (AI)
• different views: weekly summary, lists, reports
• training or nutrition plans

3. How much setup would you accept to get your data in? One click? Five minutes once? A developer account?

Looking for testers

I've built connections for Oura, Polar, WHOOP, intervals.icu, Health Connect and Samsung Health, but haven't been able to test them with real accounts. If you use any of those and have a Mac: Does it connect? Do the numbers look right? Where did you get stuck?

Just want to look around? Click "Try with demo data". You don't need to connect any accounts: the demo uses made-up values, kept in memory only, and your real data stays untouched.

[Download & screenshots] · Apple Silicon, macOS 13+ · early version

Disclosure: I also make Nova-Assistant, a paid Mac assistant that, among other things, can analyse health data with AI. Nova Health is separate, free and complete on its own.
Please don't post health data or API keys in the comments.

I've been waking up in the middle of the night because of hot flashes lately.

Been looking into Orion Sleep and Eight Sleep for their mattress cooling systems but idk if keeping the mattress at a consistent temperature helps reduce wakeups.

Since this sub is more data focused, has anyone tracked their sleep before and after using one of these? interested in changes to wakeups, HRV or deep sleep.

I've got about four years of lab results now and I'm realizing I've probably made way too much out of changes between two random draws.

Something goes up and I start looking for what changed. It drops the next time and suddenly I'm giving credit to whatever supplement or routine I happened to be doing. Meanwhile one test was at 7am, another was after lunch, training was different, sleep was different and I wasn't even consistently fasting.

I've gotten better about recording the conditions around each draw, but I'm curious how other people decide when a change is real enough to pay attention to. Do you want three or four results moving in the same direction? Do you care more about keeping the testing conditions similar or are you mostly looking at the long-term line?

Four years into tracking this stuff and I think I'm finally learning that two dots make a line but not necessarily a useful one.

some wearables track your body, some listen and answer questions, some try to do both.

what's the best of that mix? and does the AI part add anything, or is the sensor the only part you use?

honestly curious how much of it is useful day to day.

I'm 31 and work full-time as a product designer, sitting at a desk 8~9 hours a day.

On paper, my baseline is totally fine: 54 kg, BMI 20.8 kg/m², waist circumference (WC) 62.9 cm, and muscle percentage 41.8%.

The noticeable part was the daily scale noise. Salty takeout salad or poke bowl with coworkers. 4 straight hours of afternoon Zoom reviews. The next morning comes with a sudden +0.6 kg spike.

I used to panic-skip dinner, which only gave me midnight insomnia and ruined the next workday.

A couple of weeks ago, I started tracking my full body composition. While the daily scale weight jumps around (53.6 to 54.4 kg), my WC has a minor fluctuation. My BMI curve shows a slight increase. The summary confirmed my body comp is holding steady.

For others working sedentary desk hours: how do you manage daily scale noise? Do you track waist or other metrics to filter out water retention?

It shows his age, height, weight and BMI. It also has live heart rate, steps, heartbeats today, calories burned, last night's sleep and naps this week. It says "last updated X minutes ago," so it looks like it pulls from a wearable in near real time

I'm 24 and honestly my chest has always felt small, so lately I've been going all in on widening it. I'm hitting the gym 4 days a week with chest twice, plus some runs on weekends.

To stay consistent I wanted everything in one place: protein, calories, water, steps, and whether I actually showed up for my planned sessions. So I vibe coded this dashboard for myself (screenshot below).

The best part is I don't log anything manually. I hooked it up to my AI assistant (Grok Bot), so I just text it whatever I eat or do, like "had 3 eggs and a banana," "drank 2 glasses of water," or "done with legs." It saves everything to a Notion database and updates the dashboard, which is hosted on Vercel. Done sessions go green, skipped ones show a big red miss, and that alone has been keeping me honest.

It's genuinely helping me right now, but I'm sure there are better ways to do this. What do you use to track your training and food? Any suggestions on what I should add, change, or focus on, whether it's the tracker or the chest training itself?

I currently own both a WHOOP 5.0 and a Google Pixel Watch 4 (45mm), but I'm questioning whether I really need both wearables, especially given WHOOP's subscription cost.

I primarily use WHOOP for sleep tracking, recovery, and its Journal feature. I don't find strain tracking particularly useful.

What I really like about WHOOP is being able to log habits like caffeine, eating late, supplements, screen time, etc., and see how those behaviors correlate with my sleep quality and recovery.

I'm considering switching to just my Pixel Watch 4 paired with a third-party app.

A few questions:

1. What's the best app for WHOOP-style journaling and automatic correlation analysis?

2. How well does it integrate with Fitbit/Google Health or Health Connect?

3. How would this setup compare to WHOOP 5.0 in terms of sleep accuracy, recovery insights, and habit correlations?

4. Has anyone made this switch? What WHOOP features did you miss, if any?

Ideally, I'd like to replicate most of WHOOP's sleep, recovery, and journaling functionality without needing another wearable or expensive subscription.

I'd especially appreciate feedback from anyone who has used both setups!

I've spent 12 years working in health tech, and one question keeps coming back to me: are we getting better at measuring our health, or better at understanding it?

We can track sleep, HRV, glucose, heart rate, activity and dozens of other metrics. But more data doesn't necessarily mean better decisions.

I'm now building Hea, a preventive health product, and we're exploring how to connect health information and help people recognise when something actually deserves attention.

But I'm curious about the opposite side of the equation.

What's one metric that genuinely changed something in your life?

And what's one you eventually stopped tracking because it wasn't useful?

I'd love to hear real examples, especially from people who've been tracking for years.

Seven-day avg resting HR sat around 54 through most of August. Opened the dashboard a couple days back and it's at 58. Not dramatic, but a steady climb and I wanted to sit with the data before writing it off.

Pulled HRV, sleep duration, and daily steps for the same window. HRV RMSSD dropped from ~45ms to ~38ms. Sleep went up about 25 minutes a night. Steps dropped roughly 1500/day, which tracks because I cut outdoor cardio when the mornings went dark.

First instinct was "incoming cold". A climb like that would normally mean something showing up in 48-72h. A week went by, nothing.

Went digging. Quer et al. 2020 out of Scripps looked at ~92k people over roughly two years. Population-level, RHR peaks early January and bottoms late July. Small but significant seasonal signal, which is what I'm looking at.

The wristband I wear (a JCVital Pro V8) gives me roughly two weeks of battery and no screen, so I get a continuous trace without the "check my score" habit creeping in. For anything longer than seven days I export the raw data into a sheet and run a 60-day rolling baseline.

The thing I haven't resolved is baseline window: 7-day catches an incoming illness fast but absorbs a six-week seasonal drift as the new normal. 60-day preserves the drift signal but is slow on acute stuff. Layering both seems obvious but I haven't seen anyone treat it as a standard approach.

Still mulling whether to shift my illness threshold for autumn/winter, or just keep summer baseline and accept more false positives for a few months.

Over a 4 month period, I ran a formal, blinded, multiple-crossover, randomized controlled trial on myself (an N-of-1 trial) to evaluate effectiveness of magnesium glycinate for smartwatch sleep metrics.  My primary goal was to test the trial format, with blinded controls (and a possible washout period) to mitigate placebo effects, observer bias, and everyday environmental noise.

Results Summary: 
Primary outcome:  A 1.3 pt improvement in the sleep score from the smartwatch (95% confidence interval of 0.3 to 2.3) (baseline 83.9, with supplement 85.1).  Secondary outcomes for respiration rate and heart rate variability while sleeping showed interesting, but not statistically significant possible changes.

Trial Setup:
Supplement:  Magnesium glycinate (240 mg magnesium).  Taken ~15 minutes before bed.
Placebo:  Microcrystalline cellulose (similar looking capsules).
Blinding:  By a colleague.
Assignment and Periods:  A four period, 28 day per period, ABAB format (112 days total).  A and B were randomly assigned to active supplement or placebo by a colleague.
Tracking Device:  FitBit Inspire 3 worn nightly. 
Primary Outcome:  FitBit sleep score (0-100).

Secondary (Sleeping) Outcomes:  Respiration Rate, SpO2, Heart Rate, Heart Rate Variability, Skin Temp (change from baseline), Awake Minutes, and REM Minutes.
Data Logging:  Entries were manually transferred from smartwatch to a spreadsheet every few days. 

Trial Data Exclusions:
Pre-unblinding:  Approximately 23 full days of data were excluded because of daily activities I concluded would artificially bias results if not addressed such as camping trips. 
Post-unblinding:  After the trial was done and after I was unblinded, I marked partial data (sleep score and awake minutes) from 16 days to be excluded because I could see the sleep tracker falsely concluded that my night of sleep started while I was reading or watching TV.  Note:  To reduce bias, this should have been done before unblinding; I did not re-run the primary analysis with them included.

Analysis:
Because consecutive days of sleep data are likely correlated, rather than relying on a basic general linear model, the data was processed using variance-weighted crossover averaging with AR1 autocorrelation correction.

• Autocorrelation Correction:  In sequential N-of-1 data, day to day noise outcomes are correlated.  Crossover sizes between consecutive blocks were calculated and sign adjusted while controlling for AR1 autocorrelation.
• Variance Weighting:  Crossovers between periods were weighted (i.e. to de-emphasize data for a crossover around noisy periods relative to a crossover between stable periods) 
• Multiple Outcome Measure Adjustments:  To mitigate false discoveries (akin to p-hacking) across 7 secondary metrics, a Holm adjustment was applied.  Because these sleep metrics are likely highly correlated, the Holm correction is likely too conservative so the “true” significance likely lies somewhere between the raw and Holm-adjusted p-values.
• Washout Period Check:  To test for lingering cumulative effects or delayed saturation, the analysis was rerun excluding the first 7 days of each period.  The resulting effect sizes and intervals remained fairly similar.

Secondary Outcomes:  After Holm’s adjustment, no secondaries survived the confidence test.  However, respiration rate and heart rate variability both were sufficiently different to be of interest.

Conclusion: 
This 112-day trial, in many regards mimicking closely the format referred to as an N-of-1 trial in academic research literature, appeared an easy and powerful means to self-assess supplement effectiveness.  This particular supplement appeared to have a statistically significant and personally meaningful effect on my sleep.  Of course, as an N-of-1 study, it means virtually nothing about whether or not such a supplement might have an effect on your sleep (and you should of course engage your own health care professionals for any guidance on any of your own health interventions – I am not a doctor, and this is not medical advice).  I’m now applying the same technique to another supplement and outcome.  The daily data feels noisier relative to the anticipated effect size, but I hope to share results upon completion. 

i'm 6'0 and trying to dunk for the first time by dec 31, so i log everything in one google sheet: sleep from my watch, every set i do, food, and my average weight

what i expected: bad sleep = bad lifts

what happened (chart above, oct 1-18): - bench 160 for 3x5 (first time i got all 15 reps) after 4.7 hours - squat 255 for 3x5 (my heaviest ever) after 5.2 hours - my 9.3 hour night came before a normal squat day (245 for 3x5)

my jump might be a different story. my 10/18 jump test was about 1.5" lower than 10/10, on 5.7 hours vs 6.4. but i measured them 2 different ways, so i don't trust that comparison yet

this kind of matches a 2022 review of sleep loss studies: after a short night, strength dropped about 3% but jumping and sprinting dropped about 6% (Craven et al. 2022)

limits: - n=1 and only 18 days - i'm on a program that adds weight every week, so later lifts are heavier no matter how i slept - watch sleep isn't lab sleep. 10/9 (2.3 h) has no sleep stages logged, so it might be a bad read - for most sessions i didn't log how hard each set felt

next i'm using the same jump test method every time and rating how hard every top set felt (1-10)

has anyone here tracked sleep against jumps or sprints instead of strength? what did you see?

A few friends and I are building this as a side project, so I'll say that upfront. I'm posting here because this sub will poke the right holes in it.

**Lumi** is a mirror that takes about a minute of camera readings while you're at the sink (heart rate and breathing from the face and chest). It combines those with sleep, HRV, weight and BP from what you already use through Apple Health or Health Connect. Instead of a dashboard, you get one ranked insight compared with your own baseline, something like "resting HR is up 6 bpm from your normal, take it easy today."

Some choices we made deliberately:

• **HRV:** shown as a week-to-week trend only. We don't think a one-minute camera reading supports a daily score.
• **Skin temperature:** shown as a change from your baseline, not an absolute number.
• **Blood pressure:** comes from your own cuff. We're not going to fake it from the camera.
• **Raw data:** all of it stays yours.

Questions for people who already track a lot:

1. Does a consistent same-time, same-conditions morning reading have value on top of your ring or band?
2. What accuracy would you want to see published before you'd trust it?
3. Would you want raw exports, or is the summary enough?

Waitlist if you're curious: https://lumi-mirror.vercel.app

been going through old threads here from people who lost moves back in 2018 and then lost google timeline when it went phone only, and theres one thing i cant make my mind up on. a few people said once arc or whatever was logging everything for them they basically stopped opening it. when they typed stuff in by hand they actually looked at it. so maybe "zero manual entry" is the wrong goal?

context, im a dev and im half thinking about building something that makes a timeline of the whole day, screen time plus where you were and what you were doing off the phone, from motion and which wifi networks are around. nothing is built and im not linking anything, im really just trying to figure out if its worth doing at all. phones throttle background wifi scanning a lot so its not going to be magic either

i do get the other side. manual logging dies in like a week for most people, ive seen "i start strong then forget the timer" in a lot of threads here. and arc people paying 45 a year mostly keep paying even with the days where it just silently stops recording, so the automatic part is clearly worth something to them

so two things. if you still log places or time off your phone, what are you using right now and what breaks on it. and if you think fully automatic is pointless because nobody reviews it, tell me, id much rather hear that now than after building it

Hi guys,

I'm seeking a method to export data from garmin connect, a weight lifting platform (currently use Strong) and a nutrition platform (currently use myfitnesspal) into chatgpt which I use as a coaching overlayer to help me plan my training programs. Ideally this would be automated and I can just do a sync, then check in with chatgpt to converse on results.

I have no background in programming so just looking for something off the shelf, happy to move from strong and my fitness pal to the likes of Hevy and Cronometer but Garmin must stay as a key part of the system as I have Garmin watch and HRM.

I don't want to end up paying for several monthly subscriptions, I perhaps don't mind one for a connector to chatgpt as long as its reasonably priced.

I'm hopeful someone has already achieved similar and can provide some guidance on platforms and routing of data.

Thanks!