The guide, held against my data

Smartwatch Pacing

Learn to manage your chronic illness with a (Garmin) smartwatch, by Laure Wiggers, 2025/07.

A free, patient-written guide. Wiggers has had ME/CFS and POTS since 2009. Her idea: the ordinary smartwatch you already wear can be learned, slowly, into a way of reading your own body. Not an alarm, a notebook. She is explicit these are patterns to learn for yourself, not rules to apply, and that your Garmin can't warn you about everything.

Her own caution, up front: “be aware that your Garmin can't warn you about everything.”

This site takes her at her word. Wiggers asks readers to learn what is normal for their own body, and to decide for themselves whether her patterns are true for them. So that is what I do here: I run that exercise on four years of data, recorded before I had ever read her. I do it for myself, and as a worked example for others. It never grades her guide right or wrong; the only question is how much of it shows up in my body.


Why I use her guide as my framework

Reading four years of my own numbers needs a lens: some account of what these signals tend to mean for a body like mine. Hers is the one I chose. It was written by a patient, for patients, out of years spent reading the same family of watch I wear. It is free, in Dutch and English, with an audiobook. In the Netherlands it has standing: four of the most-visited Dutch patient organisations all point to it, and many people pace by it.

So using it as my lens is not only for me. Holding one real record, mine, against a guide that many people actually follow makes this a live example others can read too. I am not grading her map; I am seeing how much of it is drawn on my own body.

Where the guide is referenced

The Dutch ME/CVS Vereniging hosts the PDF directly; the Long COVID Toolkit recommends it for Garmin users; Post-COVID Nederland lists it among its tools; and MECVS Nederland recommends it as a guide made by and for people with lived experience. Internationally, the research forum Science for ME has an active thread on the English translation. The closest counterparts (Visible in the UK, MindfulPacer in Switzerland, the Workwell Foundation's clinical RHR + 15 rule) either need a dedicated device or are written for clinicians. In the patient-written, free, smartwatch-already-on-your-wrist category, Wiggers' guide is the one that exists.


Two things the watch reads, and how I read them

Her guide reads two different things off the same watch. One is the crash: the delayed bill you pay after doing too much, what doctors call post-exertional malaise, or PEM. The other is the body's moment-to-moment struggle with standing up, staying upright, blood volume and heat; this is the POTS side, the orthostatic side. Both show up in the same numbers, your heart rate, your stress, your HRV, and often on different days. These are the two things long COVID most often does that a watch can pick up: most people with long COVID report the crash, and trouble on standing is common too, though a formal POTS diagnosis fits a smaller share. Wiggers has both ME/CFS and POTS, so her guide teaches both.

This page holds her patterns against my own record, sorted by how my data can engage each one: the shapes I can recognize, the predictions I could test, and, for completeness, the parts my data cannot reach. The full statement-by-statement accounting lives in the ledger.


One

Patterns I can recognize

She names a shape; the only question here is whether that shape is in my four years. When it is, I say how often, and when. These are descriptions, not crash tests.

Stuck stress, the wall of orange

PEM · POTS · everyday

A healthy body turns its stress system on for effort and off for rest. Sometimes mine gets stuck on: the Garmin stress bars stay orange all evening, long after I have stopped and lain down. Wiggers calls it a wall of orange.

morningmiddayafternooneveningnight
rest stress high stress

The body lets go.

Stress rises to meet the afternoon, then the evening settles into rest. The autonomic system switches itself off.

Idealised Garmin stress level (0–100) through one day, 18 readings from morning to night. Not real data.
Day123456789101112131415161718
A healthy evening1215205570453022183040282015121087
After overexertion182230658072687066727880767470666055

Idealised illustration of the pattern — not real data. Mirrors Wiggers pp. 30–36.

In my data?
Yes.
When, how often
It is one of the most familiar shapes in my record, scattered across all four years. Not a rare warning sign; an ordinary kind of day.

She is clear it has many causes, and my days bear that out: a heavy or late meal, an upsetting phone call, visitors, a hot bath, a poor night. Sometimes I can name the trigger; often the wall is just there and I cannot say why, which is exactly her point. I did also run the narrow crash test, does stuck stress come before a crash, and it came back weak: this is a read of why the body will not settle, not a crash alarm.

see the finding →

The U-dip

POTS · orthostatic

Once in a while my stress does the opposite of a wall: it sinks into a deep blue dip in the middle of the day, a U shape, while the body-battery gauge quietly drifts up. It looks like a patch of lovely recovery. Wiggers knows the shape well; for her it is the moment she feels suddenly drained and wiped, and she reads it as low blood volume and takes electrolytes.

morningmiddayevening
rest (blue) stress body battery

Stress dips to blue, the battery drifts up.

It looks like the picture of recovery. But this is the moment Wiggers feels suddenly drained and wiped, which she reads as low blood volume and treats with electrolytes. I never did; yet the same shape sits in my data.

Idealised Garmin stress level and body battery (0–100) through one day, 16 readings from morning to evening. Not real data.
Reading12345678910111213141516
Stress46546058442818141624405662605448
Body battery52515050525661656767656260595857

Idealised illustration of the pattern — not real data. Mirrors Wiggers pp. 36, 46.

In my data?
Yes.
When, how often
More common in my earlier years, fading in the later ones. But those later years are also when I started an antidepressant, so I cannot cleanly separate a real easing from the medication era; and I never took up her salt-and-fluid routine, so I cannot credit the fading to managing my circulation.

One honest limit: this is not a validated reading of POTS. POTS is defined by a heart-rate jump on standing that this watch has no sensor to see. So I call it the circulation pattern Wiggers names, present on my watch, and I leave the diagnosis aside. Like the wall of orange, I checked whether it flags a coming crash; it does not, which is exactly what you would expect from something that is not about crashes.

see the finding →

Two

Predictions I could test

Here her guide makes a claim about cause and effect: this number moves, and a crash is coming. Those I can hold against my data. The verdicts are the scorecard's; the badge says how each one held up on my body.

A falling HRV, before a crash

PEM · crash axis
Showed up

Heart-rate variability, HRV, is the tiny variation in the gaps between heartbeats; more variation usually means a better-rested, more resilient body. Wiggers trusts it most: when it slides down over a few days, she reads a crash coming.

No HRV sensor on this watch — read sideways, through the night-to-night variability of sleep stress.

HRV vs. normalyour normallooks recovered8 nights7654321crash

The morning looks perfect.

A sudden high reading reads like deep recovery — but after a big push it can be the body’s false calm, and the crash follows anyway. Wiggers calls it the parasympathetic swing.

Idealised nightly heart-rate variability relative to your normal in the nights before a crash (positive = above normal). Note the deceptive high the night before. Not real data.
Night8 nights7654321crash
HRV vs. normal0.2-0.30.1-0.20.3-0.10.11.7-0.4

Idealised illustration of the pattern — not real data.

This is the one that held up best, and the strangest to earn: my 2019 watch records no HRV at all, so I had to read it sideways, through the night-to-night jitter in my sleeping stress. Even so it cleared my pre-registered bar across the whole four years. Modest, and worth watching, not a forecast.

see the finding →

The night that looks perfect, and isn't

PEM · crash axis
Partly

Body battery is Garmin's fuel gauge, refilling with rest, draining with effort. Wiggers names a trap in it: after you have badly overdone it, the watch can show a suspiciously high recovery overnight, a night that looks perfect, and a crash comes anyway. She calls it the parasympathetic swing.

A healthy night refills the battery.

You spend the day, you sleep, and the overnight recharge brings you back near full. The morning starts with reserves.

Idealised body-battery level the morning after, for two kinds of night. Not real data.
NightOvernight level (%)
A healthy night88
A deficit night24

Idealised illustration of the concept — not real data. How it actually looks in my body is the thread below.

Partly, and the split is the finding. The reassuring number a normal user would trust, a high morning battery, did not separate crashes from ordinary days. But the deeper reading underneath it, a collapse in that night-to-night jitter, held up. The body's warning is real; the watch face just shows it as good news.

see the finding →

Morning heart rate above your normal

PEM · crash axis
Not found

The most famous pacing rule there is: if your resting heart rate in the morning is up a handful of beats over your personal normal, you overdid it, so take it easy today.

heart rate (bpm)your normal · ~60morningmiddayafternoonevening

It settles back.

Heart rate rises to meet the activity, then drops back to your normal within minutes. The engine idles down.

Idealised heart rate (bpm) through one day, 12 readings from morning to evening; resting baseline about 60 bpm. Not real data.
Day123456789101112
A normal afternoon606164789588706361606059
After overdoing it6266809810099979592888480

Idealised illustration of the pattern — not real data.

On my body, mostly it just is not there: no clean rise in resting heart rate before my crashes. An honest negative for the best-known rule in pacing.

see the finding →

Too much, in the days before

PEM · crash axis
Not found

Her steps-and-activity rule: there is a personal line above which you pay for it, and you have to respect the lag, the exertion that crashes you is in the days before, not the same day.

your thresholdMonTueWedThuFriSatSun≈ 4 days latercrash

The bill comes days later.

Above your personal line, crash risk climbs — but the crash rarely lands the same day. The exertion that costs you is usually a few days back.

Idealised daily exertion across one week (personal threshold = 7); the over-threshold day is Tue, and the crash follows about four days later on Sat. Not real data.
DayMonTueWedThuFriSatSun
Exertion3943223

Idealised illustration of the pattern — not real data.

I thought this was my clearest tell. It turned out to be an artifact of how the baseline was calculated: once I fixed that, the signal vanished. An honest correction, on the board of things that did not survive.

see the finding →

The cost is not a straight line

PEM · crash axis
tested, a curve

She says a stress score of 40 is far more tiring than a 30: a small step on the chart that is a big step in the body, a kind of stair-step.

Held, with a twist. How I felt does not move in a straight line with the stress number; the relationship bends. That bend is real enough that a naive straight-line reading of it looks flat and misses it.

how I felt isn't a straight line →

Three

Predictions I could test, but haven't yet

A living scorecard has a fourth state I don't hide: patterns from her guide that my data could check, that I simply haven't run yet. Here are the ones a reader is most likely to have met in her guide; the full list is in the ledger.


Four

What my data can't reach

For completeness, so a curious reader who has read her guide is not left wondering: the patterns I can only point at. Either I never tracked the input, or this watch is blind to it. Not tested, just honestly flagged.

The everyday triggers I know but never logged

Her guide also lists the everyday things that spike a body: food, alcohol, hormones, light, noise, emotion. Some of these I recognize in myself, strongly. Alcohol: I stopped when I got ill; it makes me really tired, not worth the extra strain. Light: my sunglasses are one of the standard things I bring whenever I leave the house. Noise: headphones on, and on some days I have to shield hard against leaf blowers, traffic, a vacuum cleaner. But I never logged any of it, so here it stays anecdote, not something I can hold against data. The one everyday trigger I do chase, with the data I have, is emotional load, over on Beyond the guide .


What holding it to my data showed

The shape that held up best was a slow one, on the crash side: the night-to-night jitter in my sleeping stress. Most of the famous rules, the morning heart rate, the four-day build-up, were honest negatives for my body. The orthostatic side is real but quiet, and mostly my watch cannot see it.

None of it predicts a crash. At best it is a weather report on the body, a sense of the kind of week I am in. And it is one body's read, mine, which may not be yours.

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