COROS Menstrual Cycle Redesign
What if a fitness tracker actually understood your cycle and used it to make you a better athlete?
Hormones move on a roughly 28-day cycle, and those shifts change how you recover, how you sleep, and how training feels. The effect is real and it’s individual, which is exactly the excuse most wearables use to leave it out. COROS reads the body better than almost anyone: training load, recovery, HRV. It never clocked the one variable that reshapes all of those readings every month. The app can tell you you’re 100% recovered on a morning you feel like 20%. Wearables still default to the male body, and the cycle is exactly what that default leaves out.
Midway through, COROS shipped their own version. It confirmed what I’d wagered on: predictions that were visibly off, nothing to learn from, and no line drawn between the cycle and how you actually train. It tracks a period, not a cycle. The capability was there. The connection wasn’t.
Not my designCOROS screen 12.14.2025
Not my designCOROS screen 12.14.2025
Not my designCOROS screen 12.14.2025
Not my designCOROS screen 12.14.2025Working from the outside, I mapped the service as best I could: the frontstage from the app itself, the backstage inferred from how it behaves. Even at that resolution, it broke in the same place everywhere: there’s no loop. Data flows in (cycle length, symptoms, sleep) and nothing meaningful flows back out. The user does all the logging and gets a calendar in return. That single structural gap became the thing the entire redesign had to fix; every decision below traces back to it.
I benchmarked COROS against Garmin, WHOOP, Oura, and Fitbit, but the read that mattered wasn’t the feature grid. COROS is genuinely strong at the hard part: training load and recovery intelligence. Where it falls behind, and where most trackers do, is the last mile: turning that physiology into something a woman can act on this morning. So I didn’t set out to design a new feature. I set out to build the bridge between the intelligence COROS already has and a decision the female athlete can actually make today.
I rebuilt the service so every touchpoint has to earn its place by connecting cycle data to a training decision. The backstage stops being a passive store: it learns from behavior, sharpens its predictions over time, and finally closes the loop the audit exposed: data in, guidance back.
Six screens before the app. Click any to read it
My first instinct was to teach everything up front. If hormones reshape training, the app should explain the whole cycle before you start, so I built a heavy educational onboarding: phase by phase, hormone curves, the works.
It tested clunky. Nobody wants a biology lecture between turning the feature on and using it, and by the time people reached the dashboard they’d forgotten most of it anyway. Education wasn’t the mistake. Front-loading it was.
Onboarding got cut to what genuinely has to happen first: one idea and two questions. The intro teaches a single principle, “work with your body, not against it.” Setup asks only for your last period and average cycle length. Everything else moved into the app, surfaced the moment it’s useful. The phase insights are the part I’m proudest of: each phase explained right where you’ll use it, and honest enough to hand authority back to your own body instead of the calendar.
Drag to compare
A ring of 28 circles instead of a solid arc, one per day of the cycle. Colour carried the phase and fill carried time: solid for the days you’d already lived, outlines for the ones still ahead, and a small marker on today. The fertile days got a hatched treatment so they read like a cell instead of a swatch. That was the part I liked.
Though it was a fun idea, it did not survive the size of the screen. At watch scale the circles collapsed into noise, the boundaries between phases stopped being readable at a glance, and the palette I needed to tell four phases apart in dots failed visibility and color contrast standards. A watch face gets about a second of attention. Anything that needs a second look has already failed.
The continuous arc: four solid segments, one marker for today, and the phase named in plain text underneath. It gives up the hatching, and it passes contrast and stays legible in daylight on a wrist, which is the only condition this screen is ever read in.
Three moves carry the whole redesign. In the current app there’s no phase insight on the dashboard at all: to learn about your phase you open the cycle tracker and read a wall of text, in one place, while your training lives in another. Everything here exists to close that distance.
The Phase Insights card sits directly under your daily activity and above the training calendar. Placement is the argument. The card doesn’t ask to be visited, it sits in the path you were already walking. It names the phase in plain language, gives you sleep, strain and stress tolerance in one glance, and states the day and its confidence honestly, so a wrong prediction reads as a system still learning rather than a system that’s wrong about you. You don’t go find the connection. It’s already made.
The original feature tracked a period. It didn’t teach you anything about the cycle driving it, so education stopped being an onboarding lecture and became part of the app. Tap a phase and you get the hormone curve, what it means for sleep, strain and stress tolerance, and what to do with that today. Every phase says the same four things, so you learn the pattern once and read it forever.
The loop only works if the data keeps coming, and the phone is the wrong place to ask for it. A cycle screen on the watch shows the day and the phase at a glance, and takes a period log in two taps. Logging now happens wherever you already are, which is the difference between a feature people try and a feature people keep.
The onboarding I landed on, plus the phase insights it hands off to. Two questions, one principle, and then out of your way.
Start on the intro, set your last period and cycle length, and the dashboard opens on the phase you’re actually in. The Phase Insights card is where the whole argument lives, so tap through to a phase and read what it tells you to do today.
I started this project because I care about women’s health and believe athletes deserve tools that understand their physiology. When COROS shipped their version mid-project, I got to test my research-driven approach against what a real product team actually built. Humbling and validating at the same time.
This one stretched me. Service design was the part I didn’t expect. I’m not inside the company, so I had to read the backstage from the outside, infer it from how the product behaves, and stay honest about the parts I couldn’t know. Working at that resolution was humbling in the useful way.
It also forced me to think past the screen: the data pipelines, the CS workflows, the PM tradeoffs that quietly decide what a user ever sees. Screens were the last thing I drew, not the first. I approach every design problem differently now because of it.
If I picked it back up, the next thing I’d pressure-test is the prediction model with real users. Everything here rests on the app getting accurate enough to earn trust by cycle three, and that’s the one assumption I can’t validate alone.
This one stayed a concept, so I never got live numbers. But if it shipped, the metric I’d watch isn’t downloads; it’s whether people are still logging in month three. That’s the only real proof the loop gives something back.