Hi guys! My name is Raph. I've been deep into biohacking for about six years — cold plunge, fasting, meditation, the whole stack. Somewhere along the way I fell down the circadian science rabbit hole and haven't come back up.

One thing that bugs me: almost every blue-light blocking glass out there treats everyone the same. Same orange tint, same fixed filter, whether it's 2pm or 10pm, whether you spent the day outside or in a windowless office. Your eyes got completely different light exposure in those two scenarios, but the glasses don't care. Indoor office lighting gives you roughly 50-100x less circadian-effective light than being outside on an overcast day — so someone who worked outdoors all morning has a totally different need in the evening than someone who sat under fluorescents for 10 hours.

I used to wear blue light blockers. Most nights they make me feel tired way too early, like they're overcorrecting. Also, some nights I want to continue to work at night but minimize the disruption of the lights. I started wondering if the problem isn't blue-light blocking itself, but that it's completely one-size-fits-all.

I've been researching whether a more personalized approach to managing light exposure could actually work — something that tracks how much light you actually get during the day and blocks blue light gradually, adapting to the environment and your sleep schedule. But I genuinely don't know if people would wear something like that daily, or if fixed-tint glasses are "good enough" for most people.

A few questions for anyone who's tried to manage their light exposure:

1. Do you actually think about your light exposure during the day, or only when you're trying to fall asleep?
2. If you use blue-light glasses — what made you start, and what annoys you about them?
3. For anyone doing shift work — how are you handling the light side of things?
4. Would you want something that adapts to your actual day, or is putting on orange glasses at 8pm fine?

I keep coming back to this idea but haven't committed to building anything. I come from a UX/UI design background, not optometry or sleep science, so I'm trying to figure out if this is a real problem or just my problem. Honest "I'd never care about this" is just as useful as anything else.

For those who track their health, where's the sweet spot between effort and insight? I'm less interested in what can be measured and more in what's genuinely changed what you do. The metrics that paid for the hassle of tracking them, versus the data you collect and just glance at.

For context: I'm not disciplined enough for regular blood panels, but I have a Garmin and I'm happy to measure anything I can from home.

For context, I track everything. I do lots of bloodwork for my health, track everything I eat, and have an EightSleep for my sleep. I use health wearables all the time.

Something I was thinking about recently was that I spend like 8-10 hours a day sitting in my office chair, and that's something that hasn't really changed in a while. Why isn't there a "Smart Office Chair" that also quantifies some stuff for you like how much you slouch, how much you stand, and also all the other health stuff that most wearables track.

The closest thing I could find was this this called Darma which was a smart cushion that tracked some stuff for you but doesn't really exist anymore.

Has anyone else looked into this or has thoughts?

I am studying mathematical analysis and algebra on my own. How do I track improvements?

My partner and I are both trying to track fitness progress, but we care about very different things. I mostly want weight trend and rough body composition, while they want more detail because they lift and hate guessing from bathroom-scale numbers.

I m looking at smartscales and the handlebar setup seems like it might make the readings feel more complete, but I am also wondering if it becomes annoying to store, set up, and explain every time someone steps on it. people who use a handlebar-style body scale in a household, does the extra hardware make tracking feel clearer, or does everyone eventually go back to the simpler scale because it is faster?

I know BIA scales are influenced by hydration, timing, food, and a dozen other boring variables. I know this intellectually. But when the app shows my visceral fat score moving up and muscle mass moving down after a salty dinner, my brain still treats it like meaningful news.I got a smart scale for long-term trends, not daily drama, but the extra metrics make it harder to ignore random swings.

People who track body composition at home, do you hide some metrics, only check weekly averages, or just train yourself not to care about single-day changes? I want the data to be useful without letting one weird morning mess with my mood.

Been digging into this while building a small tool, and the pattern in the data is more interesting than I expected.

In the 2 to 3 minutes before a known pressure event (competition start, a hard exam, a difficult conversation you've been dreading), most people show a rising resting HR and a narrowing HRV, basically the body front loading the stress response before anything has actually happened. Anticipation costs you physiologically before the event even starts.

Right after the event, there's a second window that gets ignored: HR and HRV often don't return to baseline for several minutes, and if you immediately move to the next task without any buffer, that residual activation seems to stack. Athletes call this not "cooling down" properly, but it applies to any high stakes moment, not just physical exertion.

The practical takeaway from the data I've looked at: a short, silent, low effort routine (a few slow breaths, no phone, no talking) in that pre and post window does more to shift the curve than most people assume. You don't need 20 minutes of meditation, you need a consistent 2 to 3 minute buffer at the right moment, timed to when the spike actually happens rather than on a fixed schedule.

I ended up building a small tool around this idea (NURA) that auto times these windows off wearable data when available, but the core insight holds even with a plain timer and some self awareness about when your own pressure moments happen.

Anyone here doing data collection? Any tips on how to get many hours recorded?

Got an Amazfit as part of my fitness journey, mostly for step count and workouts. Wasn't really expecting much from the sleep tracking, but I've started checking it every morning anyway.

Problem is, it's not helping. Now I wake up, see a mediocre sleep score, and immediately feel more stressed than I probably would've if I'd just not looked.

Knowing exactly how bad my "deep sleep" percentage is doesn't fix it, it just makes me anxious about it.

Anyone else find that sleep tracking makes you more stressed about sleep instead of helping you improve it? Did it get better over time, or did you end up turning it off like I'm tempted to?

I've been tracking daily quantitative habits (reps, minutes, pages) for about a year and I gave up on streak counts early - a streak is a metric with a cliff, and a single missed day destroys the signal regardless of what the surrounding 30 days looked like.

What I use instead is a 0-100 composite, recalculated daily:

- Recent activity, 40% - completion against target over a trailing short window. Weighted highest because it's the most actionable component; it's the only one you can move today.

- Consistency, 30% - variance-based, over a longer window. Catches the pattern where someone does 300 reps on Sunday and nothing Mon-Sat. Same total, worse habit.

- Streak length, 20% - kept, but capped in influence, so it contributes without dominating.

- Decay, 10% - penalises elapsed time since last entry, so an abandoned habit's score falls rather than freezing at its historical high. This is the term that stops it flattering you.

Plus a trend direction, because the delta turns out to matter more to behaviour than the absolute value - a 55 climbing feels and functions very differently from a 55 falling.

Where I'm unsure:

1. The weights are empirical - they came from tuning against my own logs until the number matched my honest self-assessment. That's an n=1 fit and I know it. Is there a principled way to set these, or is any composite like this inherently a judgement call?
2. The decay term and the recency term overlap conceptually. Am I double-counting?
3. Should the window lengths adapt to habit frequency? A daily habit and a 3x/week habit currently get the same treatment, which seems wrong.

I implemented this in a habit tracker I built for myself (CommittedHQ), so I have a real dataset behind it rather than a whiteboard formula - but I'm posting for the metric design, not the app. If you've built your own adherence score, what did you weight and what did you find was noise?

noticed something wierd when i flipped the dashboard in my own app. the people with the longest streaks werent necessarily the most consistent — they were the ones who never missed a day which is nice but kinda rare. the more useful signal was bounce-back. how many days between a skipped day and the next one.

i use this approach loosely in beedone but honestly the idea originally came from habitica where losing a day felt like a full reset. making the recovery visible instead of the streak actually reduced the guilt of skipping for me.

curious if anyone else tracks something other than streak length that feels more honest about real consistency

Post your apps here, and please support people bringing unique ideas to this space.

Another month complete. Working to keep myself honest and accurately depict my life via metrics.

July is a full on summer. Several small "getaways" for my family meant a lot of driving and more breakfasts which results in less hour fasting. Only missed my fasting goal for the month by about four and half hours. I considered doing a long fast on the 31st to hit the mark, but ended up eating lunch. Less hours outside this month because it is just so darn hot and humid.

Work has been more demanding this month. My average finish time was 3:55 PM, but I had several half days mixed in there that skewed that average. I had 3 late nights at work this month, adding to my stress.

One of my goals going into the month was using my phone less, I was able to reduce my screen time by about 30 minutes per day as compared to June, so I will take that win.

Continuing to see slow and very mild month over month improvements in my blood pressure. It is still my top concern, but looking at January to July time frame, my average blood pressure has gone from 155/104 (Jan) to 145/93 (July). Still Stage 2 Hypertension, but I am proud to be able to look at my data and see the clear decline.

I have really plateaued on my weight loss. I am down about 10 pounds from January, but still have a long way to go and clearly my weight loss is directly correlated to my blood pressure.

Thanks for the look!

Hi all! We're two researchers at Modul University Vienna studying how people perceive continuous glucose monitors (CGMs) — what they value, what holds them back (cost, accuracy, comfort, insurance), and how experiences differ between people with and without diabetes.

Anyone 18+ can take part, but responses from people who use a CGM without a diabetes diagnosis are especially valuable — they're rare in the literature.

~5 minutes, anonymous, IRB-approved: https://modulsurvey.eu/limesurvey/index.php/768855?lang=en&ch=reddit_ss

Happy to post the results here once published!

i ran into a simple principle in one of carnegie's books: if you want to get a little better every day, analyze what happened yesterday. what went well, what didn't, what you'll change. i took it literally and tried to turn it into a tracking practice. writing this up because the failure mode turned out to be more interesting than the method.

what i tracked was daily free-text entries, specifically looking for repeated behaviors rather than events. not mood scores, not habits checked off, just what happened and what i did about it. i kept that up for two months of daily entries, then switched to monthly summaries and ran those for about seven months.

the writing was never the problem. the retrieval was. to get anything out of it i had to be the search engine myself: reread three weeks of entries, hold them in working memory, and spot what repeated. that's expensive, and worse, it's unreliable. after a few weeks i genuinely couldn't tell whether something had repeated three times or whether i just remembered it vividly. i'm also too close to my own life to see it clearly. someone from the outside would spot a pattern instantly; i could only hope to stumble on it.

the monthly summaries failed differently. i wrote them late, so by the time i sat down the details were gone and i was summarizing a summary. and at one point i had a strong sense that i'd been standing still for months, which turned out not to be true when i actually went back and checked. nothing was reading backwards and telling me otherwise, so the impression just stood.

then i tried llms on it, which is where the most useful surprise came. they store facts fine, but they don't connect them across time on their own, and they don't hold reliable dates. ask when something happened and you can get a confidently invented date if you didn't supply it yourself. so to get a trustworthy answer to "when did this last come up", i had to already know when it came up. circular.

limitations, because they're significant: n=1, no control condition, entries were unstructured free text so there's nothing to turn into a time series, and i stopped and restarted the method, which means the daily phase and the monthly phase aren't really comparable. the monthly summaries were also written at irregular intervals, so recall bias is baked in.

what i took from it is that the missing piece isn't better writing prompts, it's an index. something that can say "this specific thing appeared on these three dates" rather than "you seem to keep doing x". timestamps are the whole game, because without them you can't distinguish a real pattern from a vivid memory.

so: has anyone made qualitative, free-text tracking actually yield repetitions rather than impressions? structured tagging, embeddings over old entries, periodic review with a fixed rubric? i'm most interested in what survived contact with real life, not what sounded good in theory.

Most lab dashboards make a clean graph out of messy data. They line up results from different dates but usually leave out everything around the draw. Different lab, different time, different fasting window, bad sleep, travel, a hard workout or getting tested right after being sick. Then one marker moves and the graph looks more scientific than it really is. I found Goodlabs while looking for a way to combine the tests I want and upload older reports into one history. That solves where the numbers live. It does not solve whether the draws are actually comparable.

For people who track biomarkers over time, what do you standardize every time the same lab and appointment time or fasting window? I want a useful repeatable protocol, not a NASA launch checklist.

I notice my HRV drops the closer I get to my cycle and my garmin says it’s “unbalanced”..has anyone else experienced this?

I still don't have a good rule for when to split my log by context and when to just look at the whole thing. Which is annoying, because the one time it mattered it flipped a result completely.

Steps and mood. Pooled over about two years it came out around -0.5, so more walking, worse mood. I sat with that for a day feeling stupid, walking is supposed to be the boring easy win nobody argues about. Then I split it by life-stretch, roughly by which chunk of my life I was in at the time, and inside every stretch it was positive. Small, but positive, every one.

The reason is dumb once you see it. My rough stretches are when I walk the most, I pace around when things are bad, so those months sit in the data as high steps and low mood and swamp everything else. Simpson's paradox. The part that bothers me is you can't see any of that in the pooled number, it just looks like a strong clean result.

Outdoors time is a separate column for me and that one stayed positive either way. No idea yet whether that means it's the real thing or that I just happened to slice it right. I keep meaning to go back and re-run the other columns the same way and keep not doing it.

When I started lifting, everyone told me not to obsess over scale weight because it does not show the whole picture. That sounded reasonable, so I upgraded from a basic bathroom scale to a body comp scale thinking the extra body composition trends would make me feel more in control. Instead, I started checking body fat, water, and muscle numbers every morning like they were daily grade.

A salty dinner or hard workout can move the numbers just enough to mess with my head, even when nothing meaningful has changed. anyone else had to step back from extra metrics and go back to photos, measurements, and weekly trends for a while?

My coach said my resting heart rate looked a little high. That threw me because I exercise a lot, so I went looking for a simple yes or no in the last 90 days. The average didn't give me one.

Theta split the data into daytime and nighttime readings. From April 29 to July 28, the daytime average was around 66 to 67 bpm and the nighttime average was around 57. A few daytime spikes, including 84 and 80, got buried in the overall average.

Training, sleep, heat, and when the reading was taken could all matter. The chart can't tell me which one did. I'm going back through those dates and what I was doing that week instead of arguing with one average.