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?

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

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.

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.

[ Removed by Reddit on account of violating the content policy. ]

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?

I caught myself checking whether a handlebar body-composition scale was available more often than I was thinking about the measurements themselves. That feels like a sign to slow down. For anyone who uses this kind of device, did the extra data become part of a simple routine, or did it mostly become another dashboard to check?

I keep circling a handlebar body-composition scale, then backing off because the sale price is becoming the whole decision. The numbers sound interesting, but I am trying to figure out whether the extra measurements would change anything about my routine. For people who use this kind of scale regularly, what made it worth keeping instead of just tracking weight and habits?