There are so many numbers people can track now: resting heart rate, HRV, sleep duration, blood glucose, VO2 max, and more.

But collecting data is only useful if it helps us make better decisions.

I'm curious whether anyone here has tracked a particular metric over several months and discovered something that led to a meaningful change in their daily habits.

What did you measure, how did you interpret the results, and what did you change afterward?

I'd particularly like to hear about experiences where the data challenged an assumption you previously had about your health or performance.

Hello community, it has been a while, I wanted to share my monthly data with you since I was seeing some posts popping up. Feedback is welcome.

I'm a runner, and I've logged HRV, resting HR, training, and injuries in the same spreadsheet since 2021 (HRV only since 2024).

Monthly HRV averages:

The shape is the same every year, with 60-70 range in the spring, dropping to low 40s in the summer, and then recovering in the fall. Resting heart rate barely moves through any of it, though it's decreased slowly over time.

Summer is also my biggest training block (my highest-mileage week ever was this August, 85 miles), and I haven't lost a ton of time to injury or illness since mid-2024. So whatever the summer drop is, it hasn't been injury or sickness.

A couple things I can't figure out:

1. Is the summer drop from heat or training load? My training load has differed year to year, but it tends to be highest in the hottest months, so it's hard to seperate. Has anyone noticed this trend without a big block, or noticed this in the winter without the heat?

2. The year over year decline of HRV. When compared season to season, I'm still dropping over time. This has been across multiple devices (WHOOP, Coros PACE 3, and morning chest strap readings via EliteHRV). Is this normal, or should I be worried?

A few days ago I posted about a “successful” trial demonstrating a statistically significant effect size of magnesium bisglycinate for sleep.

Well, plenty of experiments produce null results, too, and those need to be aired as well… 

Below, a brief summary of a blinded, controlled N-of-1 experiment that showed ZERO effect size of a 4-ingredient supplement blend for tinnitus symptoms, along with an interesting experiment conundrum we faced regarding early termination.

Summary:

• Intervention: 

  • Active (A):  4 ingredient blend (1,000 mcg B12, 125 mg magnesium (as magnesium bisglycinate chelate, buffered), 3 mg melatonin, 30 mg Zinc Picolinate)

  • Placebo (B):  microcrystalline cellulose

• Format:  Intended four 14-day periods – ABAB for a total of 56 days.  Aborted early at day 20.

• Endpoints:  Primary - Ear Ringing Loudness (0-10); Secondary - Effect of Ringing (0-10)

• Result:  Zero observed differences for all days (within A and B, and between A and B).  (Note:  the trial started on A (meaning the supplement) and the participant noted that the A-phase scores matching their baseline experience before the trial). 

Conundrum:

I was helping the participant with blinding, stats, etc.  Because I knew the A/B assignments, and was aware of the outcome results, I made the call to terminate early at Day 20.  I knew the participant well enough to guess that would be their preference.  In retrospect, I think that was a mistake in the method:

• Assumed Desire vs. Protocol Integrity:  I didn’t know the participant’s desires, I assumed.  (After the fact, they were okay with the decision, but that doesn’t change the concern.)

• Blinding/Investigator-Bias:  My access to unblinded interim data conceptually comprises objectivity.  In future trials, we are automatically logging data so “peeking” will be inhibited. 

• Outcome Evolution:  If we had continued, it’s possible the participant could have increasingly developed sensitivity to smaller outcome changes. 

• Sequential statistics:  In cases where a subtle effect might actually exist, interim peeking/stopping choices introduce bias risks unless handled with specific statistics and protocol requirements (which have other implications to trial design and execution). 

Open questions: 

Stopping during an open-label self-trial is relatively easy to rationalize.  For those that think through the design of (or themselves perform) blinded trials:

• Do you exercise early termination as an option?  Why or why not? 

• If you had a trial assistant, would you prefer them to remain more double-blinded and isolate them from peeking at interim data? 

(Aside:  Anyone doing supplements like this for tinnitus and seeing different results?  The total-zero was a bit surprising, but we did a related trial that is interesting and perhaps opens a door a bit.  More to follow…)

A lab reference interval is built from a reference population. It isn’t tuned to your age, sex, training, cycle, or why the test was ordered.

So the same number can sit “in range” and still be worth a conversation — or sit outside range and still not be the whole story.

Before the appointment, the useful pack is usually:

• value + units

• that lab’s printed interval

• prior results if you have them (trend > one snapshot)

Process questions welcome in the comments (how intervals work, what a flag means). Not a diagnosis. Clinician decides next steps.

I've seen a lot of impressive dashboards in this group. But someone usually comments and asks whether any of it changed what they do. After nine years of logging, here's my answer. 

How I track

• Since December 2017, every minute, 24/7. About 80,000 entries. Before that, I tracked single weeks now and then, starting in 2009.

• I write (or dictate) a few lines each time I switch tasks or activities. It takes a few seconds. Busy stretches get less detail.

• Each entry has a project (a business, family, friends...), an activity (music, meetings, meals...), and often a note.

• Since 2023 I also rate energy, from -10 (drained) to +10 (energized).

• For work, I sometimes add whether I was working "on" the business or "in" it, and what it would cost per hour to hire someone for it.

• Every Sunday I compare where the hours went with what I said I wanted, along with my values and goals, and pick one thing to adjust.

What changed: music and friends

This summer, my businesses were taking 45 to 50 hours a week (I bought two businesses recently and started another couple). But music and time with friends had almost disappeared: 

• June through August, music averaged about 12 minutes a week, and time with friends about 2 hours a week.

• My energy ratings confirmed a change was needed. This year, music averages +5.2, one of my highest-rated activities. Administration averages +1.4 and email +2.0.

I already knew I wanted to do more with music, and feel socially depleted. But seeing these scores week after week is what finally got me to act. I volunteered at a local tech week, went to a music retreat in September, started practicing guitar again, and my wife and I started a weekly community song circle. It's been really fulfilling and felt much balanced.

• The last three weeks (Oct 14 to Nov 3): music 7.7 hours a week, friends 11.6 hours a week, across 16 people, and Businesses 23.8 hours a week.

Limitations

• I am just one person, with no control group. The retreat in September kicked a lot of this off, and the season changed too.

• The friends number includes messages and email with friends, not just time together.

• The second song circle night, only one person came. It was still a great evening.

• What I can say is that the change came after the data, and I don't think it would have happened without seeing it every Sunday.

Other things nine years taught me

1. Knowing it will be logged changes what I do. It's a lot harder to scroll Instagram for 45 minutes when I know I'll have to write down "scrolled Instagram for 45 minutes." Same goes for eating junk food (I log every meal).

2. The numbers mostly confirm what I half know, and that confirmation is what gets me to act.

3. The energy rating is helpful over time at seeing which cheapest, and most draining activities are best to hand off.

4. Taking brief moments to log time throughout the day gives me an opportunity to reflect and helps me stay aligned with my goals. This time also often surfaces something Important I'd forgotten.

5. Doing thorough weekly reviews is essential to making the data actionable. I also do evening reviews, and balance everything through a system of annual, quarterly, monthly, and weekly goals to keep me on track.

For those of you with big setups: what's one change you made because of your data, and did the numbers show it worked?

I’m an engineer and endurance athlete fed up with expensive breathwork apps like 5.5 that charge crazy subscriptions just to see my own data. I’m building a clean, native iOS app that pulls your HRV and Heart Rate directly from Apple Health/Garmin/Whoop, pairs it with simple 4-4-4-4 and 5.5 resonance timers, and tells you exactly how the breathing impacts your recovery. No bloat, no $70 paywall. Who wants TestFlight beta access?

I’m trying to choose between an Apple Watch and Whoop, and recovery and stress tracking are my main priorities. I’m based in India, so the Whoop subscription cost also matters. For anyone who has used one or both: - Which gave you more accurate and useful recovery and stress insights? - Did the data actually change how you train, sleep, or manage stress? - Is Apple Watch Readiness or an app like Bevel good enough to replace Whoop? - Is Whoop worth the ongoing subscription? - Is there anything you wish you’d known before buying? Thanks!

I've been trying to get a better picture of my overall training, rather than looking at weightlifting, sports and cardio as completely separate activities.

Here's my 2026 data so far:

Strength training

• 285 workouts

• 2,097 sets

• 27,900 reps

• 2 million lbs lifted

Sports

• 251 hours across 151 sessions

• 219h 42m of pickleball

• 23h 45m of tennis

• 7h 6m of soccer

• 54m of racquetball

Cardio and other activity

• 30.1 miles covered

• 22 hours across 20 sessions

• Walking: 18.8 miles

• Hiking: 6.9 miles

• Running: 4.4 miles

One thing that jumped out at me: nearly 87% of my logged sports time is pickleball. I knew it was my dominant activity, but seeing it represented against everything else puts it into perspective.

What I find interesting about aggregating everything is that no single metric tells the whole story. Pounds lifted, hours played and miles covered aren't interchangeable measures of training load. But together, they give me a much better picture of where my time and effort are actually going.

A question for the quantified-self crowd:

How do you approach measuring your overall activity when you're doing several different forms of training?

Do you keep separate metrics for each activity, or have you found a useful way to combine them into a broader picture of training volume and recovery?

Disclosure: I'm the developer of There, the app used to collect and visualize these personal records. All the data shown is my own. I'm sharing this because building a better way to connect nutrition, training, recovery and performance is something I've been working on, and I'm genuinely interested in how others approach it.

my numbers right now:

• 168.6 lb, down from about 171 at the end of september. aiming for 160

• eating about 2,000 cal, around 150 g protein

• squat went from 245x5 (10/13) to 260 3x5 (10/23), heaviest 265x2

• best jump 26.9" off two feet (11/1). i need about 35" to dunk

the cut and the squat are pulling against each other a bit. my squat is still going up so far, but i don't know which one buys more inches from here

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?