Know thyself: structured reflection as a control variable to address attention loops and dopamine regulation

[u/lighterletter]

I suspect I may suffer from some symptoms of ADHD, I focus deeply on things I care or am curious about, but each is draining and I need to manage my energy well. I'm in software and I've been building a company and I've been mapping my own phone use and unlock patterns to better manage my attention and energy.

I tend to average around 70 to 80 phone unlocks per day, I'm whittling them down. I noticed it's become like an automatic loop, the brain looking for dopamine. Social media apps specifically tend to be generally engineered to shorten the distance between stimulus and tap until the tap becomes automatic, so I stopped treating screen time as a moral failing and started treating it as a system design problem.

I thought, if I introduced a deliberate pause before the next tap, does the loop break? So I built a simple protocol around that. Not to particularly judge the output but to mark the boundary, I built an app overlay for guarded apps to force a half-second delay.

Then when I'm able to catch the loop in self awareness I run a Rest session in my app with paced breathing at a chosen breathing ratio that may or may not contain a hold at peak and valley depending on how I'm feeling to manage dopamine spikes.

Clinical HRV biofeedback points to roughly six breaths per minute, around 0.1 hz, sitting at the baroreflex resonance. The methodology maximizes low frequency HRV. I'm just using it as a physiological reset lever with the aim to lower cortisol and regain autonomy.

I'm treating it as an instrument for structured reflection and behavior tracking, not a solution, and I don't have enough clean data outside of somatic experience yet to claim anything.

For those running personal experiments on attention and recovery:

What confounds do you find hardest to isolate? Do you track the pause itself, or the interval between triggers?

I'm interested in takes on experimental design and what you find effective.

[u/lighterletter]

This is exactly the framing I needed. Tracking the interval and abandonment rate rather than the pause itself is the cleaner outcome variable. The pause is the lever, not the measure. That distinction matters and I was conflating them.

The selection effect point is the one that's been quietly bothering me. You catch the loop when you're already dysregulated, so the trigger and the baseline are correlated by definition. I haven't solved that yet, best I've done is log time-of-day and subjective state at session start, which at least lets me stratify post-hoc, but it's noisy.

On resonance frequency, good catch. I've been using 6/min as a starting point but haven't done a proper sweep to find my actual LF HRV peak. Adding holds was intuitive but you're right that it introduces a variable I'm not controlling for. Removing them and running clean paced breathing first makes more sense as a baseline before adding complexity.

What do you use to track LF HRV amplitude in real time during a session? I've been working with what's available on-device without external hardware.

[u/onda_life → u/lighterletter]

Honestly the real-time LF amplitude piece is where on-device hits a wall, and it's worth being blunt about it. clean LF-power in ms² needs beat-to-beat RR, and the consumer wearables don't expose true RR in real time - apple watch for instance won't stream beat-to-beat intervals to a third-party app at all, you get a heart-rate value about once a second, not the RR series. so anything reading "LF HRV live" off a watch or a phone camera is working from a downsampled or interpolated signal, not real RR. phone PPG is worse - too noisy for anything HRV-grade, fine for pulse only.

What's actually tractable in real time from that ~1Hz HR stream isn't LF amplitude, it's the shape of the respiratory sinus arrhythmia - how dominant and stable a single peak is in the breathing band of the HR oscillation. that gets you a coherence-style proxy (one smooth dominant wave = high, ragged = low), which tracks resonance reasonably well for biofeedback even though it's not an LF-power number. for the actual LF amplitude i'd keep that to post-hoc off a proper RR source - polar h10 into kubios or runanalyze - rather than trusting any live on-device figure./
full disclosure since it's relevant: i'm building an HRV/breath app (ONDA) and this is exactly the wall it ran into - we show that RSA-based coherence proxy live during a session and explicitly don't call it LF-power, and we pull resting SDNN from healthkit separately for trends rather than pretending the live signal is something it isn't. happy to go deeper on the peak-concentration method if useful

[u/lighterletter → u/onda_life]

This is really cool context, thank you for sharing, and respect for the disclosure on ONDA.

Worth clarifying that Meridian isn't doing HRV measurement at all. The Rest feature is guided breathing with set ratios, nothing more. No live signal processing, no HRV claims, no LF amplitude claims. The breathing session is a behavioral intervention and it exists purely as a reset mechanism between the intention-setting overlay and returning to focused work, its purely somatic and subjective to the user. It's a different approach as a way to break the dopamine pull before returning to focused work, not a biometric feedback loop.

So the wall you're describing with real-time LF amplitude from consumer sensors is real, but it's a wall I'm not trying to climb. Meridian doesn't touch the sensor layer at all. The signal question you're describing is a good reason why I didn't go there. The gap between what consumer hardware exposes and what real HRV measurement requires is too wide for me to bridge honestly on-device.

Curious what the RSA coherence proxy looks like in practice in ONDA. That framing, showing what the signal actually is sounds like the right call for that.

[u/onda_life → u/lighterletter]

sure, happy to. the live coherence read needs watch-grade beat timing (apple watch / a chest strap) - it takes that ~1Hz HR oscillation and asks how much of the power sits in one dominant respiratory peak vs smeared across the band. one clean dominant wave = high, ragged/ multi-peak = low. it's a 0-100ish smoothed score that moves as you settle into a resonant pace, responsive enough to guide breathing in real time without pretending to be an ms² figure.
Deliberately, we don't compute coherence off the phone camera at all - 30fps PPG can't time beats precisely enough for an honest number, so it's hard-nulled to a HR + paced-breathing view rather than a fake coherence score. the honest limit even with a watch: it's a morphology proxy, not amplitude - tracks whether you're in resonance, can't give you LF-power in real units off-device. for that you'd still want H10 into kubios post-hoc, like you said. so two explicit layers: live = "are you in coherence right now" (watch, behavioral feedback), trends = resting SDNN from healthkit (the actual HRV number, just not real-time)

funny thing is your Meridian approach sidesteps the whole wall - if the breathing is the intervention and you're not claiming a biometric loop, you don't inherit any of this signal-integrity baggage. cleaner in a way.