I put my whole life into a single database

I've been wearing an Apple Watch for close to 10 years. I've tracked my weight as well along those years but nothing crazy like OP. The Apple watch tracked plenty.

I had some strange symptoms and two doctors insisted I had a weak heart and potential heart failure. This was shocking! Turns out I do have a really "weak" rhythm, but heart failure is when your heart is progressively getting worse in it's pumping. I don't even remember which metric he looked at in my Apple health - but basically my heart has always been this way. A doctor looking at a single data point might think I have abnormally low blood pressure/heart rate, but if I've had this for 10 years with no change, the medical assessment is very different - it means nothing. Sometimes boring data is exactly what you need. For this reason, I will probably always wear an Apple watch (or equivalent) moving forward.

Data can feel useless for 10 years until one day it becomes critical. The benefit is spiky and uneven.

It was kinda interesting to see how many times I woke up, or track hours, but to be honest I realised after a few months that when my tracker said "You had good sleep", or "You had bad sleep" I was already aware - I woke up smiling, or grumpy depending on how I'd done.

I didn't ever look at the data and think "I want to go to bed now to catch up on the four hours I missed yesterday". I continued to have mostly consistent hours, but if I was doing something interesting I'd stay awake, and if I was tired I'd go to bed earlier naturally. The graphs and data wasn't providing anything of value, or encouraging me to change my behaviour in any significant way.

I agree with this but minimizing the cost changes the ROI.

Personally, I've discovered useful insights tracking various life metrics. But I also found quickly diminishing returns after a few weeks or months -- if an association isn't obvious within that timeframe it's either too much effort to isolate or too slow or small to matter.

At various points I've tracked calories, macronutrients, weight, allergens, supplements, sleep, exercise volume, exercise timing, nighttime screen use, spending/budget, air quality, and mood. Now I know what kind of cooking wrecks the air quality in my house, what foods I don't digest well, what various protein/carb/fat ratios look like on my plate, how much effort it takes to improve fitness, that exercise in the morning or early afternoon improves my sleep while exercise in the evening harms it, and that any alcohol or caffeine wreck my sleep while screens at night have no measurable effect. But once I understand the associations I can alter my behavior and move on.

> The whole "quantified self" movement might be more about OCD and perfectionism than anything else.

I would agree that continuing to track metrics every day long after they've stopped yielding new insights is often compulsive behavior. But I think that's an argument for time-boxing experiments, not necessarily avoiding them altogether.

Above all, it's just interesting. I enjoy reading about the day-by-day progression of a crush or my brutally honest feelings about a trip that produced stunning pictures. It weaves nuance into my history.

I do think it's not worth spending a whole lot of time on, though - hence why the first thing I did was add that mechanism to have Claude build it for me, with me mostly glancing at a plan and saying yes/no. It's the perfect thing to vibe-code - if it breaks, I revert a commit and it doesn't matter because nobody depends on it but me.

Felix's statement isn't a condemnation of quantified self. I think that sometimes, when you're applying algorithms that aren't well-studied, you get pointless or bogus data. I feel much this way about sleep tracking algorithms from the likes of Apple and Whoop. Vitals, too, although it seems remarkably good at detecting when I'm about to get sick.

As a person in the older demographic of HN, having an Apple Watch quietly collect useful data over years and store it in a database has been immensely useful in helping resolve medical issues.

I used to really be into QS, and if I'm training for a marathon, I'm still studying graphs and drawing conclusions. I'm still into QS, but now I just silently log data to Apple Health and use it for medical histories or to identify certain trends (vo2max, cardio fitness, blood pressure, etc) a couple times a year.

* Hardware companies went out of business, stopped supporting devices, etc. It became obvious that there was no long term commitment to make good quality hardware that lasted a long time.

* Many devices and/or data collection was consolidated big, data hungry companies Google and Apple. Competitors have similar anti-consumer uses of data. I don't want any of these companies to have my data.

* Related to the last one, limited to no offline or local only data collection.

It is very hard to gather most of this data with off the shelf hardware and keep your data private.

Trying to extrapolate this conclusion to the entire "quantified self" movement is not correct. The issue is the time cost, not the act itself. If a trusted company came along (as if...) that sucked up this much data to allow you to answer these questions with minimal effort, I'm sure this would be a different story.

Anything at the fringes of tech with no tried and true solution requires hundreds of hours of effort. The author's conclusions are also personal, there are other styles of living and conclusions to be drawn that change the calculus on whether to do it or not.

We already mostly know what makes people happy/healthy: personal connections, physical activity, healthy diet and some sort of purpose/goal in life that goes beyond day-to-day activities. The problem is that these things generally require (hard) work and can be unpleasant sometimes, so humans do what humans do and spend unreasonable amounts of time doing the more pleasant things such as reading and gathering info rather than applying these and what they already know. (That's not to say that a project like this can't be fun or lead to insights, especially across longer time spans, but i feel like all of the questions in the first paragraph have fairly obvious answers if you know yourself at all, that don't require extensive tracking of stats to get)

I did something similar to pull data from my Garmin watch. This meant writing all manner of code to pull data out of FIT files (interesting and often infuriating self-describing file format), coming up with schemas to hold that data to make it queryable, adding visualisations, performing analysis, pattern matching, etc.

The end result is nothing really useful, I had a bunch of scripts that semi-automated some jobs that would have taken 1 minute to do manually and only ever needed to be done a max of five times a day, but I learned a load of things along the way. Often these were useful lessons that can be applied to many other things when developing software.

In a similar vein I've gone to lots of trouble to build a cooling system for my homelab rack (ESP32 to control PWM fans, Dallas 1-wire for reading temp/humidity, exposing measurements as metrics for scraping/observability, designing things to deal with the different voltages involved, etc). I could have just gone and bought an off-the-shelf solution from AC Infinity and installed it in minutes but where would the fun in that be.

Who knows how many hours I spent scanning nutritional facts on the backs of boxes, estimating amounts of liquids, and even tracking sips of water. And weighing myself! Thank goodness I used a "smart" scale at least, and I didn't have to worry about carefully inputting my weight to an app each time.

But the whole project was an exercise in perfectionism. "I have to remember this sandwich and log it the next time I'm alone" made me anxious, but once I logged it, I felt a sense of completion. The database and my personal history are now at nirvana. Everything is complete.

All for me to learn things every human alive knows today: eat more food and you'll gain weight; eat less food and you'll lose weight. Yes, I can now tell you the exact average difference in calories I'll eat, statistically speaking, on a day that I have adderall in the morning vs a day that I don't. Yes, there's a similar (but much smaller) difference in average calories per day if I have caffeine in the morning as well. And I can tell you that I generally eat an additional 200-400 calories per day on a Fri-Sun than on a Mon-Thu. Wow, groundbreaking.

I've always had a lot of water, but matching foodanddrink.csv to my HealthKit data showed that I have more water on days that I walk more steps. Mildly interesting to see it written out for me? I guess. But was it worth cataloguing every cup of water? Absolutely not.

Was any of [gestures broadly at me pulling out my phone and cataloguing each item I ate] necessary to learn that? Of course not. It gave me a chance to look back at the database and say "Wowee! I did that! Every day for a year, wow, I'm so cool!" and not much else.

As someone who has dealt with OCD and perfectionism, I think that could be the case for a lot of people. And the urge to obsessively track everything can be debilitating.

Quantified Self, at least in the intended form, is focused on testing specific theories, not on collecting large amounts of data and trying to find something interesting in them.

See, for example:

https://gwern.net/zeo/zeo#what-qs-is-not-just-data-gathering

On the other hand, if you are sick like me, charting your long term heath data from doctors visits and photographing skin issues can lead to great discoveries. I have been diagnosed with Erythrocytosis and a susceptibility to mycobacteria infection which caused pulmonary nodules and skin lesions. Only after showing my data collection to my doctor. Since I have mental illness they constantly over looked my physical issues so I needed hard data to convince them of my ideas.

For those curious, I have an minor IL12B deficiency and a partial immune deficiency leading to mildly elevated levels of DexoyATP which is partially corrected with zinc supplements.

Shaming individuals doesn't seem to be productive or helpful.

Air travel works for people if the benefits outweigh the costs. The only thing that changes behavior is to change the costs.

And even if costs were 10x there are still plenty of people who will fly tons, because it would still be economically productive. There are always going to be people who fly 10x more than others, because certain jobs and roles simply require it.

I get that you may have to see family abroad or maybe indulge for a holiday, but this is "I'm using an airplane to commute" kind of level.

And here I am trying to book my train tickets to go to London instead of flying even though it costs three times as much just to avoid a few kg of CO2 (among other things), it's making me angry.

https://apps.apple.com/us/app/reflect-track-anything/id64638...

It's possible the friction could be reduced here by having some kind of Generative AI try to help capture data, but then you'd have to verify that it was being done correctly... honestly, I think it's simply not practical for most people to do this.

Real life is messy. How much time you want to spend on recordkeeping to make it seem less messy or make you feel like you have more control is up to you. But sometimes it's better to embrace the mess and let go of control, in my opinion. Chances are, no one's going to care about whatever you do here in 100 years. YMMV as always.

The database is the data, collected into a coherent structure.

The database management system is (optionally) software. :)

Don’t ask how I know…