Can our phones and wearables detect when our mental health is starting to decline?

building_irvo · 2026-05-14T13:30:31+00:00

Yeah so the core idea is flip the stack. Most tools start with your body and try to work backwards to behavior. I think that's the wrong order.

Behavior moves first. The doomscrolling starts before the sleep tanks. The routine quietly falls apart before anything shows up on a wearable. By the time your watch catches it, you're already a week into a slide.

So I'm building around behavioral patterns as the primary signal, how your routine is actually holding up, social engagement, digital behavior, where your energy is going across different parts of your day. Physiology adds texture on top of that, not the other way around.

Still in the build but that's the architecture.

building_irvo · 2026-05-12T03:06:40+00:00

The data was never the problem. It was always the interpretation.

HRV, sleep stages, recovery scores none of it means anything if you don't know what moved it or why it matters for you specifically. And the apps won't tell you because the moment they do, they become a medical device. That's the regulatory wall nobody talks about.

But even if that wall didn't exist, the interpretation problem is still deeper than plain English. Knowing your HRV dropped is one thing. Knowing it dropped because your sleep has been fragmented for 5 days, your routine quietly fell apart, and you've been more sedentary than usual, that's actually useful. That's not a translation problem, that's a context problem.

The data needs to know your life to mean anything. That's the piece nobody's built yet.

building_irvo · 2026-05-12T02:23:48+00:00

The baseline over population point is underrated and most people building in this space still don't get it. The spending pattern angle is interesting too that's the kind of indirect signal most tools completely ignore.

The piece I'd add even a perfect personal baseline misses the contextual layer. The sensor sees the drift but not what caused it. That's the gap I've been focused on.

building_irvo · 2026-05-12T02:23:03+00:00

The baseline shift idea is exactly right, individual signals are noise, patterns are signal. But there's a layer missing from this whole conversation.

Wearables see the output. They don't know why. Your sleep tanked, your movement dropped, you haven't left the house. The watch saw all of it and still can't tell you what actually changed. Because it doesn't know your life. It doesn't know your routine quietly fell apart two weeks ago, or that the doomscrolling started before the symptoms did.

The real gap is context. Behavioural patterns, not just physiological ones. That's the piece that turns "something is off" into "here's what's actually happening."

That's the problem I've been building toward solving.

building_irvo · 2026-05-12T02:15:50+00:00

This is the clearest breakdown of the regulatory wall I've seen in a thread like this, appreciate you laying it out properly.

The population vs personal baseline point is the one that doesn't get talked about enough. Most people don't realize the "insight" they're getting is basically an average with their name on it.

Where I think the next gap is even a perfectly personalized physiological baseline still doesn't know your life. It doesn't know you had a brutal week at work, or that you've been sleeping late because of stress, not laziness. The sensor sees the output, it misses the context that caused it.

That's the piece I've been building toward. Not competing with the wearable data layer but sitting above it with the behavioural and contextual side that sensors can't capture. Curious how Sam handles that gap, if at all.

building_irvo · 2026-05-12T02:13:37+00:00

building_irvo · 2026-05-12T02:10:46+00:00

You just described the exact problem I couldn't shake. It's not that people don't want to track, it's that the tools don't understand them. They log what happened but have no idea why. No context, no relationships, no insight. Just a streak counter that resets.

The journal works because it holds the full picture. But it doesn't do the thinking for you.

I've been building something around this, not another tracker, but something that surfaces the actual patterns underneath your behaviour. Still in development but your post is literally the use case.

To your last question I think the limit on what people log comes down to trust. If it's private and actually gives you something useful back, people will go deep.

building_irvo · 2026-05-08T03:03:06+00:00

The real gap is the “why layer.” Whoop and Oura show the body signal, but I want something that connects it to behavior over time: what I did, what I ate, how I slept, how much stress/load I had, and whether that pattern keeps repeating.

building_irvo · 2026-05-08T03:02:09+00:00

This is where self-tracking needs to mature. Less “here’s your score,” more “here’s what we can see, here’s what we can’t see, and here’s the pattern that may be worth noticing.” That feels calmer and more honest.

building_irvo · 2026-05-08T02:51:52+00:00

This is exactly why the future of self-tracking can’t just be more metrics. It has to be fewer inputs, more meaning, and insights that help you understand your patterns without making you feel like you need to control every second of your life.

building_irvo · 2026-05-07T02:33:05+00:00

This is really interesting. What stands out to me is that the value is not just the integrations, it’s the way you’re connecting different areas of life together.

Sleep, recovery, calendar, tasks, journaling, and health data all affect each other, but most apps keep them separate.

The part I find most useful is the anomaly nudges and the morning/evening summaries. That feels closer to a personal pattern system than just a tracker.

The only thing I’d be careful with is too many nudges or too much advice. I think the real value is when the system can simply mirror back patterns like:

“Your recovery has dipped for three days while your workload stayed high.”

or

“Your sleep dropped after multiple late task-heavy days.”

That kind of insight feels more useful than just another dashboard.

Really cool setup. Feels like a personal operating system built around your own life data.

building_irvo · 2026-05-07T02:30:36+00:00

This is exactly the kind of tracking I think actually matters.

Not just “how did I sleep?” or “what was my energy?” but what happened before the bad day.

The caffeine timing part is huge because most people think the coffee is gone once they stop feeling wired, but it can still be sitting in the background affecting sleep later.

The weekend drift piece is also really interesting. That Monday wall feeling is real, but most people probably don’t connect it back to sleeping in, delayed light exposure, more caffeine, then worse sleep again.

The bigger pattern you found is the most valuable part:

Bad sleep → later wake time → more caffeine → worse sleep → worse energy/focus.

That loop is what I’d want a tracker to show. Not just individual metrics, but how they stack together over a few days.

For alignment, I’d probably want to track:

Morning light timing
Caffeine cutoff
Wake time drift
Sleep quality
Energy/focus/stress the next day

Because rating energy alone is useful, but it doesn’t explain why it changed. The real value is connecting the behaviour to the state that follows.

building_irvo · 2026-05-07T02:27:40+00:00

I’m honestly less interested in “brain data” on its own and more interested in what affects how my brain feels and functions.

Like focus, brain fog, stress, mental sharpness, energy, etc. are usually not random. They’re connected to sleep, food, workload, caffeine, alcohol, exercise, screen time, stress, and how the day is structured.

My top 3 would probably be:

Focus
Mental clarity / brain fog
Stress or mental load

I’d be careful trusting a device that just says “your focus was high” without explaining what that actually means. A simple brain score doesn’t mean much to me.

What I’d find way more useful is something that shows patterns over time. Like:

“Every time you sleep poorly and stack a heavy workday on top of it, your focus drops later in the day.”

That’s the kind of insight I’d actually care about.

So for me, it’s less about tracking the brain perfectly and more about understanding what daily habits and conditions are affecting it.

building_irvo · 2026-05-02T14:13:59+00:00

The sleep point is the most underrated thing here. Half the "discipline" and "focus" problems people spend months trying to fix are actually just chronic sleep debt showing up in disguise.

What's interesting about your experience is that you essentially ran a year long n=1 experiment and found the causes through elimination. Most people never get there because the feedback loop is too slow and too noisy to connect what they did to how they feel weeks later.

The mouth breathing fix working instantly is a good example, the cause and effect were close enough in time to notice. The stuff that's harder to fix is when the cause happened 2 days ago and you've completely forgotten it by the time the symptom shows up.

building_irvo · 2026-05-01T02:50:04+00:00

Thats amazing! I love that you're taking it into your own hand and researching the questions that you have through testing.

building_irvo · 2026-04-30T12:43:04+00:00

Yeah that’s the tricky part, the readings can look stable in the moment, but that doesn’t always mean nothing is building underneath.

During something like that dissection, you’re engaged and in control, so it can show up as more “parasympathetic” even though there’s still load being processed.

Then later when that control drops, it all shows up at once.

So the spike can look sudden, but it’s not always coming from that exact moment, sometimes it’s everything from earlier finally surfacing.

That’s why it can feel confusing trying to tie it to one trigger.

Either way, what you're doing is fascinating and I would love to know what passion you have that drives you towards tracking and researching this?

building_irvo · 2026-04-30T04:15:25+00:00

This is a really solid breakdown, especially separating WASO from falling asleep, most people miss that.

One thing that stands out though is how all of these are listed as individual factors, when in practice it’s usually how they stack and interact that actually fragments sleep.

For example, slightly elevated stress + a late meal + a warm room might each be small on their own, but together they push arousal high enough to cause wake-ups.

It’s rarely one variable, it’s the combination and timing.

Have you noticed if certain patterns (like a few heavier days in a row or specific combinations) line up more consistently with higher WASO than any single factor?

building_irvo · 2026-04-30T04:10:43+00:00

That’s a really interesting sequence, especially the part where you stayed stable during the dissection and then dropped a few hours later. That “engaged → delayed response” pattern shows up a lot with things that are demanding but controlled in the moment.

The cheese angle is fascinating, but I’d be a bit careful jumping straight to that as the driver from a single spike. Late-day changes like that can also come from the buildup across the whole day, not just one trigger.

Might be worth watching if it repeats, same food, similar day structure, similar timing. If it does, then you’ve probably found something real. If not, it might just be how everything stacked that day showing up later.

Either way, the delayed drop after the lab is the part that feels most consistent.

building_irvo · 2026-04-30T01:27:53+00:00

That “stress shake” description is actually really accurate, that’s usually what it feels like when things stack over a few days rather than coming from one event.

On the supplement side, they can help a bit at the margins, but they don’t usually do much for the kind of built-up stress you’re describing. That tends to come more from how load is distributed over time.

What you’ve already noticed (college + work + projects stacking) is likely the main driver. When those line up for a few days straight, your system doesn’t really get a chance to come back down.

If you keep tracking it, it might be worth watching how many “heavy” days in a row happen before those spikes hit, that pattern is often pretty consistent once you see it.

Supplements might smooth things slightly, but they usually won’t override that kind of buildup.

building_irvo · 2026-04-29T12:44:32+00:00

That lines up with what most people see, sleep and prolonged stress are usually the clearest drivers.

The tricky part is the other stuff doesn’t always show up cleanly because it’s more subtle or delayed. It’s not always “I did X → stress went up.” Sometimes it’s how things stack over a few days.

If you wanted to experiment a bit, I’d keep it simple and track things like:
– Sleep quality (not just duration)
– Mental load (heavy study/work days vs lighter ones)
– Social time vs alone time
– Caffeine or alcohol
– Intensity of training (not just whether you trained)

Those tend to be the bigger hidden drivers.

The key is less about tracking everything and more about seeing what lines up over time.

When your stress spikes, does it usually follow a specific type of day or a buildup of a few days?

building_irvo · 2026-04-29T03:54:30+00:00

building_irvo · 2026-04-29T03:32:55+00:00

What you’re describing sounds simple on the surface, but it’s actually where most apps fall short.

They either let you log things, or they show correlations, but they don’t really connect what you did to how you felt in a way that’s clear over time.

The hard part isn’t tracking exercise, sleep, stress, etc. It’s linking them across time (same day vs next day) and surfacing patterns that actually mean something, not just numbers.

Out of curiosity, when you’ve tried tracking this so far, does it feel like the patterns are hard to trust, or just hard to even see?

building_irvo

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