How it works

Many anonymous puzzle pieces become your personal answer.

mypacing is not a fitness tracker. It's a shared tool: everyone who takes part contributes a few data points anonymously — and that's exactly what the “MIE” (mypacing Intelligence Engine) learns from, to understand what precedes a crash and what helps. Here you can see, completely transparently, how that works. And what's actually being analyzed right now.

Live statistics

What the “MIE” is analyzing right now

Real, continuously updated counts of analyzed data points — not of people. We deliberately don't show user numbers: this is about the data that's voluntarily and anonymously shared so everyone gets clearer answers.

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anonymously analyzed data points in total

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Hours of heart-rate data

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HRV measurements

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Nights of sleep

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Activity days

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Temperature measurements

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Humidity measurements

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Analyzed measurement days

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Marked crash days

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Blood pressure measurements

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Oxygen saturation measurements

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Respiratory rate measurements

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Extended activity readings

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Body measurement readings

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Symptom reports

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Cycle reports (voluntary)

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computed crash days (prediction)

Loading … · These numbers grow with every new anonymous contribution — for example, if someone uploads a day with continuously recorded heart rate, that hour counts here live.

Honestly: if the numbers here are still small, we're just getting started. Every anonymous contribution makes the “MIE” smarter — and the number bigger.

More on this on the blog: how mypacing uses wearable data for crash analysis

In three steps: from the crowd to you

You share your daily values anonymously

Only the analyzed daily values from your upload are used — with a random ID instead of your name, an age band instead of your birth date. No name, no email, no device number. A single data point says little on its own — like one piece of a puzzle.

The “MIE” puts the anonymous pieces together

Only when many pieces come together do patterns emerge that no one person could ever see alone: When do crashes cluster? What preceded them one or two days earlier — more activity, less sleep, more heat? What did the days that went better for many people have in common? The “MIE” computes these patterns from the pooled, anonymous data — always in groups, never from individual people.

The patterns sharpen YOUR personal recommendation

What the crowd reveals flows back into your own analysis: your pacing limit, your early warning, your recommendations become more precise the more people take part. If your group turns out to be especially sensitive to heat, for example, mypacing weights that factor more heavily for you specifically. Always as an honest assessment — never as a prediction, never as a diagnosis.

The more anonymous puzzle pieces, the clearer the picture — for every single person. That's the whole point of the community.

Exactly which data is processed

Full transparency — these are the data sets that flow into the analysis (anonymously). Nothing more.

Resting heart rate	The basis of the pacing limit (resting heart rate + 15) and a sign of recovery or overload.
Heart rate throughout the day	How much time is spent above the exertion limit — the core of pacing.
HRV	Heart rate variability as a measure of how well the autonomic nervous system is recovering.
Sleep	Duration and quality — unrefreshing sleep is closely linked to crashes.
Activity & walking heart rate	Steps (deduplicated across sources, no double-counting between phone and wearable), active calories, distance, floors climbed and exertion as a possible trigger for PEM/crashes.
Blood pressure, oxygen saturation & respiratory rate	Additional circulatory and breathing values, where your device records them — further signs of recovery or overload.
Body measurements	Weight, body fat percentage, BMI — slow-moving baseline values, not day-to-day triggers, but part of the overall picture.
Temperature & humidity	Environmental factors at your approximate location — heat often intensifies symptoms.
Symptoms	Symptoms you voluntarily log in Apple Health (e.g. exhaustion, dizziness, headache) — help place patterns in context.
Cycle data (voluntary)	Only if you log it in Apple Health and grant permission — for some people it can be linked to crash patterns.
Crash markers	Crash days you mark yourself — they're what makes the patterns learnable in the first place.
Interventions & triggers	What helped and what came before — voluntary, by category.

Resting heart rate

HRV

Sleep

Activity

Blood pressure

SpO2

Respiratory rate

Body measurements

Temperature

Humidity

Symptoms

Cycle (voluntary)

Crash markers

And always, without exception: anonymous. Your values only ever flow into the “MIE” pseudonymized and in groups — never individually traceable, never sold, never used for advertising. Storage is encrypted, on servers in Germany/the EU. Public analyses only appear once a minimum group size is reached, so no one is identifiable. That's a condition, not an afterthought — without anonymity we wouldn't build this at all.

More on this on the blog: how mypacing analyzes HRV and heart-rate data from your wearable

Why we show this so openly

Because trust is everything in this project. Anyone who uses mypacing extends a double advance of trust: with a possible donation — and with their health data. Both are precious. That's why you should always be able to see what for: every data point above is a piece of shared hope that together we can find answers no one could find alone. No corporation profits from it. It belongs to the people who use it.

And if you'd like, help make sure it can keep going:

❤ Support mypacing

mypacing is not a medical device and does not provide a diagnosis. All patterns are observations from anonymous data, not a prediction. In case of symptoms, medical advice takes precedence. In an emergency, call your local emergency number.