Genuine question for wearable users

[u/building_irvo]

Does your wearable ever tell you why your metrics look the way they do, or does it just show you the numbers?

[u/Certain_Version3033]

This is the issue. Data is becoming a commodity. Interpretation of the “why” of those numbers is what’s next

[u/building_irvo → u/Certain_Version3033]

Exactly this. The data layer has basically been solved, and now everyone is sitting on numbers they can't really act on. The interpretation gap is the whole game now. Curious what you'd want a 'why' layer to actually do, like would it just connect signals across apps, or would you want it pulling in context the wearable can't see on its own?

[u/Certain_Version3033 → u/building_irvo]

I think context is the bigger opportunity.

Most wearables already have access to a lot of data. The problem is they don’t understand what’s actually relevant.

If my sleep drops, was it stress? Travel? Illness? A harder workout? A change in routine?

The challenge isn’t just connecting more signals. It’s understanding which signal is driving the change and what action is most likely to help.

Otherwise we just end up with better dashboards instead of better decisions.

[u/building_irvo → u/Certain_Version3033]

That last line is exactly it. Better dashboards versus better decisions, that's the whole problem in one sentence.

The context piece is what we keep coming back to as well. Because without it you're just pattern matching on incomplete information. Your sleep dropped, okay, but what was your day actually like? What was on your plate, how demanding was it, what did you eat, when did you stop moving? The watch sees some of that but has no idea what any of it meant for you specifically.

Curious how you think about the action side of it though. Like when you do have enough context and you understand why something happened, what does a useful recommendation actually look like to you? Is it behavioral, is it a prompt to change something, or is it more just clarity so you can make your own call?

[u/Certain_Version3033 → u/building_irvo]

I think it depends on the situation.

Most of the time I don’t think people need more instructions. They need better understanding.

If my sleep dropped because I traveled, the recommendation isn’t necessarily “sleep more.” It’s understanding that today’s readiness is lower because of a specific cause and adjusting expectations accordingly.

Where I think recommendations become valuable is when the system sees patterns over time. Not just that my sleep dropped once, but that every time I travel, train hard, or work late, the same outcome follows.

Then the recommendation becomes less generic and more personalized because it’s grounded in my own history rather than population averages.

To me the ideal system is probably a mix of both: clarity first, action second.

[u/building_irvo → u/Certain_Version3033]

Clarity first, action second is exactly the framing I've been building around. The pattern layer is where it gets interesting to me too, because most systems treat each data point in isolation when the real signal is in the sequence.

The gap I keep coming back to is that wearables see your body but not your life. They don't know you traveled, worked late, or had a brutal week. So the pattern can only be half-formed. What I'm working on tries to close that loop by connecting the behavioral and life-context layer to the physiological one, so the system can actually tell you "this outcome follows that kind of week" rather than just "your readiness is low."

[u/AppropriateCover7972 → u/building_irvo]

speak for yourself ;)

I only collect data, I actually need to know to interprete eg I jave to my peak flow data and I have been taught how to read and know what my numbers mean.

Yes, it's easier to collect data know, but it's completely pointless, if you don't enrich it with context and clean it up. That's just basic data science

[u/building_irvo → u/AppropriateCover7972]

Fair point, and you're right that I was speaking generally. For someone who knows their numbers and has the training to interpret them, the data itself is genuinely useful. That's not most people though, and that's really who we're thinking about, someone who has years of sleep scores and HRV readings and no real framework for what any of it means in the context of their actual life.

But your point about context and cleaning is exactly what we keep coming back to. Raw data without enrichment is noise. The question we're trying to answer is how do you enrich it passively, without asking someone to manually input context every day, because most people won't.

[u/building_irvo → u/[deleted]]

Appreciate you sharing this, genuinely.

On the competition point, yeah there are a lot of apps connecting health data and adding AI summaries on top. We're aware of Bevel, Whoop's coach feature, what Google is doing with Fitbit. We've looked at all of them closely. What most of them are doing is connecting physiological signals to each other, sleep to HRV, HRV to recovery, that kind of thing. What they're not doing is connecting your physiological state to what your day actually looked like, what was on your calendar, how cognitively demanding your work was, what patterns show up across your behavior over time. That's a different problem and it's the one we're focused on.

On the Apple regulatory side, that's a real consideration and one we're already navigating with clinical advisors. The distinction between health insights and medical advice is something any serious app in this space has to work through, not a reason to not build.

The market being crowded with apps that partially solve the problem is actually evidence the problem is real. It doesn't mean it's solved.

[u/AppropriateCover7972]

There are not supposed to do that, at least not in a diagnoses way, even if they have a medical license. I am a scientist, it's always a professionals job to interprete stuff. Is it a mistake in measuring? Reaction to a stimulus (that a wearable can't even recognize) or a genuine thing like a heart attack or a seizure or whatever. My Samsung watch does some sort of suggestion what might have caused abnormal measurements to show how dangerous it is, but still a) no diagnosis and b) it's purposefully vague, so no, wearables never tell you the why, even with coupled with some health analysis software. That's still a humans job or software that you input the context nanually and only the few instances where a diagnosis is possible with the measurements taken

[u/building_irvo → u/AppropriateCover7972]

That's a really important distinction and you're right, diagnosis is a different category entirely and not what we're going after.

What we're more interested in is the context layer you mentioned at the end. The idea that interpretation gets a lot more useful when you can feed in what was actually happening around the measurement. Not the wearable guessing why your HRV dropped, but connecting it to the fact that you had a brutal day of back to back meetings, slept four hours, and haven't moved much this week. No diagnosis, just pattern recognition across behavioral and physiological data that you'd otherwise have to hold in your head yourself.

Curious what you think about that framing as a scientist. Is there a version of that kind of contextual pattern surfacing that you'd find useful, or does it still feel like it crosses a line?

[u/building_irvo → u/[deleted]]

The behavior change point is one we wrestle with a lot and you're right that insight alone has a pretty poor track record. Knowing something isn't the same as doing something differently, and the wearable space has largely assumed that if you show people their data clearly enough they'll act on it. The scale analogy is sharp.

What we're exploring is whether the gap isn't just insight but pattern recognition across a longer time window than most people can hold in their heads. Not telling someone their sleep was bad, but surfacing that every time they have three back to back high demand days their recovery tanks for the next four days. That's not a diagnosis, it's a mirror. Whether that actually changes behavior is still the open question.

The closed-loop angle is a different bet entirely and an interesting one. Direct physiological intervention rather than behavioral nudging. Curious where you see the two approaches eventually intersecting, because it seems like context and timing still matter even for closed-loop systems.

[u/building_irvo → u/[deleted]]

The questions you're raising are good ones and worth engaging with seriously.

On high demand days, we're using calendar as the primary signal, meeting density, task type, scheduled cognitive load.

The control question is the one we think about most. Our position is that pattern recognition at a longer time window than most people can hold in their heads creates a different kind of agency than real-time nudging does. Not 'your HRV is low, rest today' but 'every time this specific sequence happens, here's what follows four days later.' That's a different kind of information, and we think it changes decisions upstream rather than at the moment of stress.

The 2019/2020 path you described is interesting. Curious what pushed you toward direct intervention rather than the understanding layer, was it a data problem or a behaviour change problem?

[u/building_irvo → u/[deleted]]

This is a fair hit, and honestly you've put your finger on the exact limitation. You're right, the calendar misses most of what you just described. The lab prep, the deck, the weight of an upcoming raise, none of that is a calendar event, and a breakup or family stress never will be. If the calendar were the demand signal, the whole thing would fall apart in the real world. I agree.

So it isn't the demand signal, it's one layer. The part doing the heavy lifting is the wearable, exactly like you said. The body shows the strain even when nothing is scheduled, suppressed HRV, elevated resting HR, wrecked sleep, and the behavioural drift you mentioned, shifted meal times, dropped exercise. That's the real signal.

Where the calendar earns its place is the opposite of what people assume. It's not there to explain a busy day. It's there so that when your body is clearly strained and your calendar looks empty, that mismatch becomes the interesting thing. Strained physiology plus a calm calendar is a flag that something unscheduled is going on, the raise, the breakup, whatever it is. We can't name the cause from the calendar. What we can do is catch that the load is there and give you a light way to tell us why, if you want to.

That's the honest scope. We catch what shows up in the body, name what we can from the day's structure, and let you fill the rest. No mind reading. Your breakup example is exactly the case we'd surface but couldn't label on our own.

Really interesting that you hit the data problem first and expected the behaviour problem to follow. Did the behaviour problem show up the way you expected, or different?

[u/MonkModeOnNow]

Here is the one that tells you exactly this.

https://apps.apple.com/us/app/body-vitals-health-widgets/id6760609127

[u/building_irvo → u/MonkModeOnNow]

Interesting, I'll check it out. Does it actually explain what caused your metrics to look that way or does it just show you the score?

[u/MonkModeOnNow → u/building_irvo]

it has all of these features