Every drink tracker I have looked at logs in whole "drinks" and quietly assumes the canonical strengths behind the US standard drink: 12 oz of beer at 5%, 5 oz of wine at 12%, 1.5 oz of spirits at 40%, all of which come to 14 g of ethanol. NIAAA's own page on it notes that alcohol by volume "varies within and across beverage types". I wanted to know how much, so I measured it instead of guessing.

What I pulled

The complete Open Food Facts CSV export, downloaded this morning: https://static.openfoodfacts.org/data/en.openfoodfacts.org.products.csv.gz

4,535,553 products scanned. I kept rows tagged as alcoholic beverages that carry a numeric alcohol_100g, deduplicated by barcode, and dropped values outside a plausible band per category (beer 0 to 20%, cider 0 to 15%, wine 4 to 24%, spirits 15 to 80%). That leaves 7,715 products: 3,551 beers, 2,548 wines, 827 spirits, 455 liqueurs, 334 ciders. Ethanol mass is volume x ABV x 0.789.

The presets are wrong more often than they are right, and beer is the worst case

Spirits cluster because 40% is close to a legal fixture, and wine clusters because fermentation runs out of sugar. Beer's strength is a product decision, so it is spread across the entire range.

Holding the bottle size constant, so the only variable left is strength

330 ml is the single most common beer container in the corpus, 47.8% of every beer that states a volume. Take only those, n = 1,594:

The median bottle is exactly one standard drink, which is why the equivalence survives. It is right about a third of the time.

The part that argues against my own conclusion

Per drink the error is large. Per week it mostly cancels. Drawing ten bottles at random from that distribution, a week you logged as 10.0 standard drinks is really 10.3 at the median and 8.9 to 11.7 at p10 to p90. Only 5.8% of simulated weeks are undercounted by 2 or more.

That cancellation depends entirely on drawing independently, and nobody drinks that way. If you have one beer you buy, and it is at the p90 of 8%, you are not sampling a distribution. You are undercounting by 49% every week, forever, and no amount of averaging fixes it. So the per-drink precision is close to worthless for a varied drinker and is the whole ballgame for a repetitive one, which is most people.

Limitations, and one error I made

• Catalogue frequency is not drinking frequency. One craft SKU at 8% counts the same as Heineken. Named brands inside this same corpus: Heineken 5.0% (n=31), Carlsberg 5.0% (n=20), Stella 4.9% (n=8), Budweiser 4.8% (n=8), Peroni 4.8% (n=10), Corona 4.5% (n=11), Guinness 4.2% (n=11). Every one sits at or below the 5.4% median, so the catalogue is biased upward and the consumption-weighted spread is narrower than what I measured.
• This is a European shelf. By country tag the corpus is 60.6% France and 20.1% Germany. 330 ml is also not the US 12 oz can; multiply by 1.076 for that.
• Having the ABV field filled in is itself selective, toward better documented products.
• The error: my first pass read the quantity string as a container, so "5 L" (a 20x25cl case) and "396 cl" (a 12x33cl lot) came through as single bottles and pushed p90 to 2.43 standard drinks. Hand reading the top twelve caught it. Every number above is from the single 330 ml container cut.

Worth disclosing since it is where this came from: I build a drink log called Nightjar, which takes an exact pour volume and an ABV to one decimal rather than a preset, and I went looking for evidence that the exactness earns its place. What I found is that it earns its place for one kind of drinker and not the other. https://BigBalli.com/Nightjar/

So, for people who have actually tracked this. Do you log real ABV, or a preset? And has anyone compared their own logged total against a real count of what left the house over the same period, which is the only external check on any of this I can think of?

every day i opened Notion, moved the boxes around, and somehow felt done already. beedone probably helped me notice i was tracking the ramp-up more than the work itself. the wierd part is the minute i stopped polishing the log, i started the thing faster. idk why that was harder than the spreadsheet. anyone else do this or is it just me?

I record a short spoken entry most nights. It gets transcribed on the phone, scored for sentiment, and stored with whatever sleep and step data the phone already has. Standard enough.

The part I keep going back and forth on is what it's allowed to tell me.

Most self-tracking tools surface a correlation the moment they can compute one. Two good nights and two bad ones and you get a chart. That's where most of the garbage comes from, and I've been the person nodding at a trend line drawn through six data points.

So I built thresholds in. Sleep against sentiment needs at least three nights on each side of the split before it will say anything. Movement needs four days either side, and it splits at my own median rather than some generic step target, because the question is how I compare to me. Under those counts it shows nothing at all. Not a greyed-out chart, not a "keep logging to unlock." Nothing.

The result was months of silence, which was uncomfortable in a way I didn't anticipate. I'd built a thing whose main job was to not tell me things. A few times I went back to the threshold constants intending to lower them, and couldn't come up with a defensible reason beyond wanting output.

The trade I still can't resolve: a threshold high enough to be trustworthy is also high enough that you might quit before reaching it, and a tool nobody uses long enough has an effective accuracy of zero.

Before we dive in, here are some of the most surprising findings:

• Alcohol makes me happier and doesn’t affect my sleep, happiness, or productivity the next day.
• Ramen and chips ~3×'d my irritability intensity. Ovulating ~3×'d my grumpiness frequency.
• Polyamory doesn’t hurt my emotional well-being (surprising to me) but it dramatically reduces my life satisfaction.
• Antidepressants probably gave me depression.
• 2020 was actually my best year on record. More on this later in the post.
• Weather totally affects my mood, specifically, grey overcast skies. Good thing I spent most of my life in the Pacific Northwest, a place famed for its sunniness.
• Starting a charity approximately bajillion x’ed my mentions of the word “stressed”.
• Meditation works for me - only when it’s a new meditation technique. Then the effect fades and only comes back if I try a new technique.
• Cannabis, despite making me very happy in the moment, does not affect my mood overall, one way or the other.
• Drugs, meditative states, and Christmas are the source of practically all of my peak days. Work accomplishments don’t show up in this list.
• Polyamory and conflict (related) are the source of practically all of my worst days.
• Largely my mental health is unpredictable and data analysis falls short of what I’d like to know.

Alcohol makes me happier, despite “what the science says”.

There’s currently a big fad amongst high achievers to stop drinking. All of the studies at a population level say that drinking as much alcohol as I do is unhealthy, and even if it doesn’t feel like it’s affecting my sleep, it does.

And yet - my analysis found that alcohol makes me happier and it doesn’t affect my sleep.

I’ve got an Oura ring and if anything, my sleep is better when I drink. I have better sleep onset, I don’t have the micro-awakenings or changes in the phases that everybody says happens.

I am just as happy and productive the next day as I am if I didn’t drink.

And overall, I’m happier. It doesn’t just not affect my happiness. I am, in fact, happier when I drink regularly.

I’ve tracked my emotions and a million other factors almost every day for the last 11 years. Before I go to sleep every night, I write in my “journal” (the qualitative cell in the spreadsheet), and then fill out various things in other columns like:

• Emotional well-being
• Life satisfaction (importantly different)
• Energy levels
• Productivity levels
• Irritability
• Physical symptoms (e.g. headache, cough, etc)
• What meds or supplements I’m taking
• What I ate
• And many other things, depending on what hypothesis I’m testing at the time

So I can actually see alcohol’s effects.

Also, since I’ve tracked for so long, I can actually compare periods of time where I didn’t drink at all to the ones I drank every day.

And the results are loud and clear: periods where I drink every day I record as happier. Productivity stays the same. Irritability stays the same.

How does this square with all the science saying this shouldn’t happen?

Am I just a college student, coasting on my youth?

Thanks for asking, but I’m 36. Depending on your age, that will sound like a crone or like a baby, but I think I’m definitely past the “I’m just relying on my youth.”

It could be that all of the worst health effects show up later. I think this is plausible. This is why I have a yearly liver scan done. Liver damage is the first detectable sign of alcohol’s long term damage, and I’m intending on cutting down once I see any damage.

As it is right now, my liver scans show a pristine liver.

It could also be that a lot of the long term health outcomes are driven by the outliers. While I technically drink more than a lot of scientists say is healthy, I only drink socially in the evenings, I pretty much never drink enough to pass out, vomit, make bad decisions, or have a hangover (except New Years, which is allowed imo). I also drink drinks optimized for lower immediate effects, mostly vodka sodas. Drinks with more sugar or particulates (such as red wine) are more likely to cause the immediate negative effects.

Meanwhile, I’ve watched documentaries about alcoholics, and they’re regularly day-drinking, drinking alone, passing out, and the whole shebang. They’re drinking multiple bottles of wine equivalents a day. It could very well be that most of the stats about liver disease and the like are driven by the truly problematic drinkers.

Anyways, the underlying interesting thing here is that what happens on average isn’t necessarily what happens to you. On average, women are shorter than men, but that doesn’t mean there aren’t women who are taller than men. And if you track things, you can see what works for you instead of the population on average.

Ramen and chips triples my irritability intensity. Birth control stopping ovulation reduces irritability frequency.

First off, I don’t mean eating ramen like a normal human or like in Japanese cuisine. I’m not cooking it and adding vegetables and whole foods. I buy the bags, break the noodles into a crumble, then eat it raw with the seasoning, like a modern barbarian.

It’s basically a bomb of refined carbs, salt, and ultra-processed foods (UPFs).

I don’t really care for sweets. But I’m a total salt junky. I will sometimes “snack” on just powdered soup stock.

So ramen and chips used to be my go-to comfort food.

I probably ate them every other day, usually as a full meal. (Good policy by the way. If you’re going to eat junk, make it the whole meal, so you don’t just add calories to your diet.)

I tried all sorts of moderation techniques. I didn’t store them in the house. I banned myself from convenience stores on the way to work. I set limits.

Nothing worked.

Chips and ramen are as crack to me.

So I did what one must do with crack - you must go cold turkey.

A little over a year ago I quit them entirely. No more chips and ramen for me. For life. Ever. Exceptions are something I cannot trust myself to not wiggle through.

Now, I track irritability, because I sometimes have waves of 1-7 days of irritability, where everything bothers me. Other people breathing, chewing, disagreeing with me, or even just being around me makes me crave murder.

Of course, murder is wrong, and I have impulse control, so murder is not had. But man, do I crave it.

After I quit ramen and chips, the number of days I rated a 5/10 irritability or higher went from 5% to 0.5%. Four out of ten days went from 9% of days to 3%. I still occasionally got irritable, but it’s never as intense as it was before.

I don’t know what caused this. It’s not the refined carbs. I still eat plenty of those. It’s just not in the form of ramen and chips.

Could it be some sort of chemical used as a flavoring in one or both of them? This seems plausible to me. A bunch of UPFs are associated with mood issues among children. No reason to not expect that to generalize to us crones.

The intensity went down due to ramen and chips, but I still often had low grade irritability. Days I rated a ≥2/10 irritability went from 27% of days to 21%, so practically the same.

But for the last 6 months, it’s gone down to only 7% of days. And it’s because I stopped ovulating.

On purpose mind you. I had studied my data and realized I don’t get PMS or moody when I’m menstruating (weirdly, those tend to actually be my best part of the cycle). I get moods from ovulating. My brain responds poorly to the sudden spike and drop of all the various hormones. So I experimented with taking birth control that suppresses ovulation (not all birth control does this).

Et voila! Frequency went down to ⅓ of its previous rate.

(It also coincidentally got rid of my occasional pimples as an unexpected bonus. Apparently this is such a common side effect it’s a regular recommendation from dermatologists. Who knew!)

The lesson you can pull from this is if you have mood issues, do experiment with your diet. Both adding good things and taking away bad things. A friend of mine halved her anxiety (which she’s struggled with her whole life) by starting to take an iron supplement.

It might just be that late night snack which is why you’re breaking up with your partner.

Likewise, if you’re a woman, consider taking an ovulating-suppressing birth control. Going through your cycle once a month for most of your life isn’t “natural” anyways. Hunter gatherers typically only had roughly 100 cycles in their lifetime, compared to the roughly 450 times modern industrial women go through.

Polyamory tanks my life and relationship satisfaction

I actually started tracking my emotions in 2015 because I wanted to prove to my husband that, actually, him falling in love and sleeping with other women made me unhappy. Then I just kept tracking because I’m an information hoarder.

I’ve written before about how I think polyamory is net negative for most (but not all) people who try it, so I won’t dive into it here.

Suffice to say, what’s interesting in my data analysis is that, despite my worst days being caused by poly (there ain’t no drama like poly drama), it didn’t actually affect my average day. Weirdly, one of my best months was during massive poly drama.

However

It tanked my relationship satisfaction and life satisfaction. Relationship satisfaction for the obvious reasons. I’m not a jealous or insecure person, otherwise I wouldn’t have tried polyamory in the first place. But it turns out your husband having sex and falling in love with another woman is just inherently insecurity inducing.

And for good reason! He always said that we could stop anytime I said the word. I always told him it was easy for him to say that given he wasn’t in love with somebody at the moment, but that would change.

And I was right.

Yay?

The other interesting thing is the difference between emotional well-being and life satisfaction.

Emotional well-being is the balance of positive to negative emotions in a day.

Life satisfaction is asking yourself “on a scale of 1 to 10, how satisfied are you with your life?”.

The two come apart quite often. Somebody might experience emotional well-being while playing video games all day, but their life satisfaction is likely low. Somebody might experience low emotional well-being waking up in the middle of the night to feed their baby, but their life satisfaction is likely high.

What was interesting about my experience of polyamory is that it did make me grow, in the sense of it forced me to pull out the big guns when it came to coping. During that great month in the midst of poly insanity, I exercised like a motherfucker. More cardio than ever in my life. I meditated more often then than almost any other time. Whenever I felt upset, I’d go for a run then go to the Tesco and buy smoothie ingredients, then meditate for 30 minutes. I worked at a treadmill desk, walking 4+ hours a day. I cultivated a thriving social life to distract me and support me during one of the worst periods of my life.

So I was “happy” in the sense of being able to overcome the negative emotions using every trick I knew.

But I was not happy with it.

I didn’t want to have to spend half my waking hours coping. I wanted my relationship to be a source of comfort, not something I had to cope with.

The thing that finally got me was when I did a thought experiment - what if I had a magic wand and I could simply not have polyamory make me unhappy?

I wouldn’t want that.

Because I just overall didn’t want to be polyamorous, despite having been poly for 7 years.

I didn’t want the intrinsically more drama than monogamy. I wanted somebody who wanted to just be with me. I didn’t want the incessant negotiation and emotional processing of it all. Even if I was emotionally fine with it, there’s always at least two other people involved, and I’d still have to deal with their unpredictable issues.

So I left.

I’ve been in a happy monogamous relationship for 6 years now and I’ve had less relationship drama in that entire time than I had in any random month while being poly. I was reminded that I’m actually a confident secure woman in relationships. My emotional well-being is about the same, but my life satisfaction and relationship satisfaction has reliably been high ever since.

So if you’re considering polyamory but you’re not sure, make sure to not just read the books written by people who like it. Read about people who had a bad experience with it too. Books about a topic are usually only written by people who are true believers. Look for the articles and tweets of people who hated it, it exploded their relationship, and they left and never looked back.

Also, make sure to distinguish between emotional well being and life satisfaction. They can point at very different things.

2020 was my best year and it’s a mystery as to why

2020 should have been bad. I got divorced. I was unemployed. I had no permanent home or even city I lived in. I was in a motorcycle accident in Uganda and was in pain and couldn’t walk for 4 months. Oh yeah, and there was a pandemic, if you remember that?

And yet it was by far my best year.

What the fuck?

My answer is roughly:

1. Yeah man, I have no idea
2. But here are some theories

One interesting thing - divorce doesn’t actually make people less happy. Just before the divorce causes immense suffering. But post-divorce people tend to be far happier than their baseline.

This reminds me of a question my mom, who’s twice divorced, asks people when they say they got divorced:

“Is that good or bad news?”

Absolutely, divorce can be very bad for those affected. But it also can have been deeply the right decision.

For me, the relationship had been causing me massive suffering prior (due to polyamory plus other factors I’m not going to discuss publicly because I still care for and respect the guy). So leaving led to relief rather than suffering.

It also helped that I did everything I could think of to make sure I wasn’t making a mistake. We brainstormed dozens of possible fixes, ranging from couples therapy to meditation retreats to applying mindfulness to it to trying different polyamory configurations. We prioritized them based on probability of working and ease of doing and worked systematically through the most promising ones. This was not a spontaneous decision after a bad fight. This was a rational decision come to over months to years, depending on how you count it.

I had also been doing this for pretty much all of the 7 years of being poly. So at a certain point, you have to say that you know what, I can’t change my internal state. I’ve got to change my external environment.

So when I got divorced, I felt awesome.

Same thing happened for employment. I had been running charities for the past 7 years, and while that gave me good life satisfaction, it was also stressful as fuck. So, unemployment is usually a negative factor, but for me, it was a relief. Especially since I had savings and was still working on projects I thought were important, I just wasn’t getting paid for it. So I still had purpose, money, and self-respect, which are usually the reasons why unemployment is bad for people’s mental health.

Additionally, the pandemic never stressed me out. I worked in global health. When it started, I was living in a place that was having an active bubonic plague outbreak and had just declared itself “1 month ebola-free!”. Covid seemed and continues to seem pretty mild to me. But it’s all about your reference.

My final theory is that it was the mood stabilizer I was on (lamotrigine) combined with having low external stressors.

I had started the mood stabilizer during my deepest depression in 2019 (more on that later), and it seems likely that it pulled me out of my depression. However, there were a lot of confounding factors, and I was still dealing with poly drama the likes of which you have never seen if you’re monogamous. So I just went back to my regular levels of happiness. Lamotrigine might work well for me, but it can’t make going through poly drama fun. It’s no miracle cure.

However, once I was divorced and stepped down from my org, I no longer had those stressors.

I went from 28% of my days being rated as bad in 2019 to 8% in all of 2020. My average over the rest of my years has been ~22%.

What’s interesting is that I didn’t have more good days. I just cut off the bad ones.

Which is exactly what lamotrigine is supposed to do. Most mood stabilizers cut off the troughs and the peaks. They’re usually for people with bipolar, where they struggle with depression and mania. Lamotrigine is the only one that just gets rid of the lows.

I was on lamotrigine for all of 2020, and I had only two really bad days the entire year. One of which was a cancer scare (turns out it was fine). The other one was having to deal with the poly drama aftershocks one last time.

The problem is that I came off of lamotrigine right around the same time I started my next charity. So it’s unclear whether going back to charity entrepreneurship was what caused it, or the meds.

I’ve since stepped back from starting and running orgs, and I still have ~20% bad days, so that’s some evidence against it. I’m now trying lamotrigine again, thanks to this data, but it’s still too soon to tell.

Overall though, the interesting thing about this is that tracking can help you uncover your unusual times. And that external circumstances don’t necessarily always affect you the way you’d predict.

Antidepressants probably gave me depression

In early 2019 I experienced depression so severe I had to stop working for months. I was taking antidepressants and they weren’t working.

Except. . . it’s not that they “weren’t working”. As best as I can tell, they were the cause of the depression.

You know how on the bottle it lists possible side effects, and one of the possible side effects is depression? Well, that’s probably what happened to me.

“But why did you start taking antidepressants, Kat? Surely it just overlaps because you only take them when you’re depressed, right?”

Good question. And that’s what I thought too.

But then I looked at the data.

And it told an entirely different story.

Yes, I started taking antidepressants after a particularly bad week.

But that week was actually just a regular bad week according to the data. I get moods. I’m a woman. Or I’m on the bipolar spectrum. Or something. Anyways, it’s actually totally normal for me to have stints of 1-7 days of feeling low motivation, energy, and mood.

The difference was this time, a friend recommended I try antidepressants.

And that’s when I went from my usual waves of sadness to a tsunami. I couldn’t work. I contemplated suicide. I would experience the most beautiful and loving things and feel nothing but despair. I even had a brief day of paranoia, where I thought people were watching me on cameras. It went from 24% of bad days to 66%. It went from 4% of days I rated a 4/10 emotional well-being to 32%

It lasted for about 3 months. I tried one anti-depressant then another. Neither “worked”. And it was only when I came off of them that suddenly I was cured.

Of course, there were also confounds. I moved back to my hometown instead of living in London, which I had only moved to after being heavily pressured to by my totalizing ethics and totalizing community. But then I moved back to London, and I stayed cured.

I don’t know what lesson to take from this. My policy is to try psych meds, and if you have unacceptable side effects, just stop. The problem with this one is that it was causing the symptoms they were trying to cure, so it can be hard to know. Also, you have to experiment with psych meds for weeks to months (including the titrating on and off of them), and months of suffering is a lot.

Make of that what you will.

In fact, this might be what the conclusion of all this analysis is: I got a lot out of tracking all of this. Alcohol is fine for me, ramen and chips are bad for me, ovulating messes me up, emotional well-being and life satisfaction matter, but also in the end, psychology is too complicated for us right now. I still don’t know for sure what caused the depression in 2019. Despite having tracked it for over a year, I still don’t know if a psych med helped or not.

Overall, I recommend tracking. But it is by no means a cheat code that will help you understand yourself. It is just one tool among many. But you might find out that seemingly innocuous habits are bad for you, and that things that feel like vices are actually good for you in particular.

i'm so tired of mainstream chatbots that sound like corporate customer service reps telling me to take deep breaths and validate my feelings. i tried a few of the popular open source models too, but they kept losing the thread, no real memory, no cohesion after a few messages. i recently downloaded copymind because i heard it runs on some kind of dedicated ai twin setup instead of the standard generic therapy templates. so far it's been surprisingly quiet and analytical, which fits how my brain actually works. it doesn't give cheesy motivational advice and it's not trying to be your virtual friend either. instead it remembers stuff i mentioned weeks ago and uses that to point out my logical blind spots when ii'm venting. it's the first ai tool that's actually felt useful instead of like a gimmick. anyone else found good use cases for this specific setup?

Not financial advice, just a data comparison. My country's insurance portal tracks what it paid toward my covered care automatically. I also track every yen I actually spend on health stuff in a budgeting app. Put the two side by side for four years running and they were wildly different — most years by a factor of 3-6x. Curious if anyone else has compared an institutional health dataset against their own personal tracking and found a similar gap.

I am starting a new time tracking cycle today (my cycles run 13 weeks at a time). I have tracked everything from 15-minute increments to 10-minute increments to a low as 1-minute increments. This cycle I am going to do 6-minutes, which I have never done before. This is an attempt to make some of my reporting easier (I can use decimals easier; every 6 minutes is 0.1 in my reporting).

Curious about what increments other time trackers out there use.

I am looking for some QS or self tracking podcasts to listen to and I am having zero luck. Any suggestions? Bonus points if you have any audiobook recommendations as well (but I doubt there are any).

A note before the post: my English is not good enough for a text like this, so I used an AI tool to get it from my German into English. The reading of the paper, the numbers and the opinions are mine; the tool did the language.

If you keep long-running daily logs you know the shape: a clean run, one missed day, then a stretch of nothing. I always read that as my motivation failing. A 2023 paper in the Journal of Consumer Research says a good part of it is the log itself, and the effect is bigger than I expected.

The paper. Jackie Silverman and Alixandra Barasch, "On or Off Track: How (Broken) Streaks Affect Consumer Decisions", JCR 49(6), 2023. Seven studies, just under 5,000 participants in total. The DOI is 10.1093/jcr/ucac029; an accepted-manuscript PDF turns up on Google Scholar if you do not have journal access.

What they did.

Study 1 was a field study: 980 university employees in a 30-day challenge to walk 7,000 steps a day (fall 2018), tracked with a step counter synced to an app that showed "days in a row" in a private log. On a given day, people were more likely to hit 7,000 when yesterday continued an intact streak than when yesterday had just broken one. In their logistic model the day after a break carried a coefficient of -1.01, which works out to roughly a third of the odds of hitting the target compared to a neutral day; an intact streak added +0.25. Correlational, the authors say so themselves, which is why the other six are experiments.

Studies 2 to 7 used a tracker app built into the survey, 600 to 800 people each. The trick that makes the paper interesting: everyone did exactly the same things. The only difference was what the log showed. In Study 2 all participants did four real strength exercises; in one condition the app "failed to log" one of them, so the log displayed a broken streak. Then a real choice: another strength exercise, or a stretch instead. Intact log: 66% chose the exercise. Broken log: 58%. Same four exercises done, in both groups.

Study 3 (Portuguese vocabulary) is the one I would show anyone who doubts that the display matters. It crossed the streak with whether the log was shown at all. When the log was shown: 92% kept going after an intact streak, 45% after a broken one. When the same people did the same things but no log was displayed: 65% vs 61%, barely a difference. Showing the intact streak helped, showing the broken streak hurt, and without the display the break almost did not matter.

Study 5 (games) found the same in a version where the streak was intact or broken purely by categorization, i.e. whether a completed action "counted" toward the streak or not: 83% vs 68%.

Why. The authors' explanation, backed by mediation analyses: keeping the logged streak becomes a goal in itself, separate from the thing you were originally tracking. When it breaks, the sense of accomplishment drops, and the next action is less likely.

Two moderators worth knowing.

Self-blame makes it worse (Study 6). Intact streak: 53% continued. Broken because the app blocked the fourth game: 42%. Broken because you failed the fourth game yourself: 29%. The intact group and the self-blame group had played exactly the same games, including the same nearly impossible fourth one; only whether attempts or successes "counted" differed. As a side finding, 45% said they would watch an ad to keep a streak and 43% to repair one, which is the streak-freeze business model in one number.

Repair helps, but does not restore (Study 7). Intact: 93% continued. Broken: 69%. Broken with a repair option: 85%. The authors' guess is that a repaired streak feels less authentic than one that never broke.

A public vs private log made no significant difference (supplementary study, n = 604).

Limitations, in their own words and mine. Study 1 cannot separate the streak from the kind of person who has one. The lab streaks were at most 20 items long, so nothing here speaks to 100- or 1,000-day streaks, where the effect could be larger or could flip into boredom. The paper tests intact-versus-broken; whether a broken streak is worse than no log at all was not consistently significant across studies. Participants were MTurk workers doing short tasks, not people tracking sleep for five years. And in about half the studies the logging was automatic rather than something you do by hand; the authors suspect manual logging could strengthen the effect, but did not test it.

What I take from it as someone who tracks things.

1. The gap after a miss is partly a measurement artifact. If the instrument shows you a broken chain, the next data point is less likely to exist. The display is changing the thing it displays.
2. "What counts" is a design decision with a measurable behavioral cost. Studies 5 and 6 broke streaks by changing the counting rule, not the behavior.
3. If you are the type to blame yourself for a miss, a tool that frames the miss as failure is running the worst-case condition on you.
4. Practical options: hide the chain, or pick a unit that a single miss cannot break. Weekly targets ("3 of 7") instead of daily chains, and a way to mark a planned rest day as skipped rather than missed, so it does not show up as a failure in your own analysis later.

Curious whether anyone here can see this in their own long-run data: after a missed day, is the following week thinner than the one before the miss? And does it differ between tools that show a chain and tools that do not?

Disclosure: I build an Android habit tracker around exactly this, no streak counter, weekly targets, skips excluded from the count, and the paper is cited on its site. sevengrid.app. None of the above needs it; a sheet of paper with seven columns implements the same idea.

I have this product idea to combine fitness, wellness data from our watches and fitbits and combine it with lab reports. The idea is to have a one stop data source for every user to track their health metrics:

1. How well are they trending on key health metrics as per lab reports?

2. How is my daily routine like sleep, steps, stress levels etc impacting my biomarkers?

3. Dos and don'ts personalized to me by AI

4. Game based chasing to improve health metrics by the AI who makes the plan and drops in achievements to follow the plan

Sleep scores, readiness scores, HRV, and other metrics can reveal patterns we might otherwise miss.

But they can also change how we feel about the day before it even starts. A low score can make a decent morning feel worse, while a high score may not match how exhausted we actually feel.

Have recovery metrics improved your body awareness, or made it harder to trust how you feel?

Hi everyone!

I just started using a data aggregator for all my health data, like my Whoop data, labs, Apple Health data, etc. I think the insights are cool, and they are pretty accurate as far as I'm aware. I think the coolest thing is that it's able to look at all my data, look at the timelines, and develop trends using data from each of the sources and link them together. And then give me actionable insights to improve my health.

I'm just wondering if anyone has used something like this, and if they have had positive experiences with it. :)

I’ve been looking at screenless fitness trackers like WHOOP and the Helio Strap, and I’m curious how people actually decide which one to buy.

If you use one:
What did you mainly want it for?
Which other devices did you consider?
What finally made you choose this one?
Now that you’ve used it, what do you like or dislike—and would you choose it again?
Short answers are completely fine. I’m just interested in hearing about real experiences.

We're exploring this idea and I'd like some brutally honest feedback before we build too much.

The concept is a wearable sensor & app where:

• your data stays on your phone
• the software/tooling is open source
• developers can build modules
• users choose which modules they install: sleep, running, recovery, training, cycling etc

This isn't a simple project, and we're under no illusion that building it will be easy. We're very early and are trying to figure out whether the underlying idea is actually valuable before going too far.

Would you actually use this?

And if you already use something like Whoop/Oura/Garmin/etc., what's the biggest thing you wish you could change?

I created a small presentation website with a discord invite link. If anyone is interested on giving feedback, i can share it.

I’ve been thinking a lot about how work, sleep and mental health interact over time, so I built this interactive tool called the Wellbeing Index.

You enter your work hours per day and days per week. Sleep hours per night and happiness and chaos levels. Then it calculates a wellbeing score (0–100), resilience rating, and projects a 10-year outlook based on your current habits. It also highlights weak spots. 

https://traffictorch.github.io/productivity-bond-model/wellbeing-index.html

i started measuring deep work minutes like it was the missing ingredient. not sure why i believed the tracker would shame me into doing it.

but the wierd part was i kept checking the app, tweaking the Notion schedule, then calling that progress.

in beedone i saw the same pattern. people dont just do the quest, they polish the trail theyre on. i ended up trying something dumber: stop staring at totals and just ask if i actually started the next step.

anyone else end up babysitting a metric instead of doing the work and then feeling annoyed at yourself about it? (dont answer with some perfect system either lol)

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

I posted a few weeks ago to share my bloodwork results from last year. Those results showed a very strong heart health (APOB, Cholesterol Profile, triglycerides etc) and metabolic (5.3% A1c), but concerns around high-inflammation, low ferritin, low vitamin D, low free testosterone.

I drew updated results and there are some interesting updates. A few markers trended worse but overall the cardio and metabolic markers are in a good spot. The smaller negative movements are probably from reducing cardio.

My total testosterone meaningfully increased, but unfortunately it didn't translate to higher Free T due to high SHBG. My interventions prior to this test were significantly increased strength training, reduced cardio from 12h/week to 5h/week. It seems like those changes helped to increase Total T but it was moot with the high SHBG.

My next move is adding Boron to see if I can reduce SHBG to increase free T, starting with 6mg/daily and a retest in a month.

Ferritin also barely moved despite starting supplementation every other day, I only went from 41 -> 42. I'm moving my supplementation to the morning and being stricter about adherence. This could be a nutrient absorption issue if I'm not able to move it.

Open to ideas on the focus areas, especially decreasing SHBG and increase ferritin.

Drop a 🙋 if that's you.

Started wearing a continuous glucose monitor mostly to have more data about myself, no diagnosis prompting it. Checked it on a whim after lunch one day: 240 mg/dL. Felt totally fine. Was in a meeting, normal conversation, zero symptoms.

The gap between the number and how I actually felt was unsettling once I sat with it. Anyone else tracking something (glucose or otherwise) where the data and the subjective experience just don't line up at all? Curious how people reconcile that.

i keep testing too early and then reading noise as if it were signal. the advice ranges from six weeks to three months and i cannot tell how much of that is just caution. i want to change one variable at a time and actually be able to attribute the result. mostly curious what interval you trust and whether one specific product ever gave you a change clean enough to read.

I built a workout tracker for myself a while back (offline-first, logs supplements and habits alongside training) and I’ve been using it daily for months. Lately I’ve been wondering if it’s worth turning into something other people could use.
The thing that bugs me: I use Strong for logging my sets, my Apple Watch for recovery, and then some other app or a notes doc for supplements and how I’m actually feeling day to day. None of it talks to each other.
I looked into the AI-coach apps (SensAI, Vora) that claim to solve this, but the reviews I’m seeing suggest the AI layer is hit or miss, losing context, feeling gimmicky.
Before I sink real time into building this out, is this actually a problem for other people, or am I just weird about tracking things? What do you currently use, and what’s annoying about it?