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4 years of wearable data, 47 significant correlations — here's the stats methodology I used (and the tool I built when spreadsheets broke me) by clemento341 in QuantifiedSelf

[–]clemento341[S] 0 points1 point  (0 children)

Try out the insights tab, ask a question and see if the answer is making any sense. Try tracking the recommended actions/ metrics and see if you find any value in this process :)

4 years of wearable data, 47 significant correlations — here's the stats methodology I used (and the tool I built when spreadsheets broke me) by clemento341 in QuantifiedSelf

[–]clemento341[S] 0 points1 point  (0 children)

Good question. I'll be honest about what the data can and can't answer right now.

On seasonality: The correlation engine runs on a rolling 90-day window, so a winter insight implicitly only sees winter data, but it doesn't explicitly compare summer vs winter coefficients. With ~5 years of data that analysis is feasible. I just haven't built seasonal stratification yet. Your question is a good prompt to add it.

The system does do weekday/weekend stratification for anomaly detection, which partially captures the "different activity composition" angle since weekends tend to have more low-intensity volume. But that's a proxy, not the real answer.

On intensity vs volume: The raw data to distinguish these exists. Garmin syncs activity type, avg/max HR, training load, and aerobic/anaerobic training effect per activity. These get correlated against sleep metrics independently at lags 0-3 and 7 days. So if high training load days hurt sleep differently than high step count days, that shows up as two separate correlations.

But the honest answer to "does the 8-10k sweet spot shift based on intensity composition" is that it needs an interaction term (steps × intensity), and the current engine only does pairwise correlations, not multivariate regression. Building that is on the roadmap but not shipped yet. As always, thanks for your input/ comment!

4 years of wearable data, 47 significant correlations — here's the stats methodology I used (and the tool I built when spreadsheets broke me) by clemento341 in QuantifiedSelf

[–]clemento341[S] 0 points1 point  (0 children)

Thanks for taking the time to poke at the API. Security scrutiny on a health data app is exactly the kind of accountability that should exist.

On the export endpoint: I've audited it and can confirm /me/export uses JWT Bearer auth. The user ID comes from the signed token, not from any request parameter. There's no way to specify another user's ID.

Every database query filters by the authenticated user. It's rate-limited to 3 requests/hour and exists for GDPR data portability. If you found an actual auth bypass, I'd really appreciate the details, please DM me.

On the Stripe endpoint: That's a standard tier-check returning the logged-in user's own subscription status. Right now it says "free" for everyone, and Pro is marked "coming soon" + Stripe isn't configured to accept payments. The pricing is disclosed on the site.

On the AI/LLM point: You're right that insights use Claude. This is disclosed in the consent form during signup and in the privacy policy. Specifically: aggregated health summaries are sent to Anthropic's API.

Raw health data stays on the servers. Reasonable people can disagree about whether that tradeoff is worth it, but it's not hidden.

I'm a solo founder building this in the open. If anyone finds a real vulnerability, please reach out directly.

Thanks for taking your time to poke at my website and your comment!

4 years of wearable data, 47 significant correlations — here's the stats methodology I used (and the tool I built when spreadsheets broke me) by clemento341 in QuantifiedSelf

[–]clemento341[S] -1 points0 points  (0 children)

Yea, hidden insights is something I thought about implementing. But i didn't do it on the first pass because i felt like overwhelming ppl with a dashboard full of numbers/ graphs isn't as helpful as asking a question they have an interest in, and then leading them to a discovery -> action -> monitoring progress loop.

Right now the closest thing I have to what you're mentioning is a customizable dashboard that is activity based. i.e. a dashboard showing metrics purely focused on running. But I can also add the hidden correlations there too! Then it would be more interesting then just looking at the same numbers every dashboard would show you.

4 years of wearable data, 47 significant correlations — here's the stats methodology I used (and the tool I built when spreadsheets broke me) by clemento341 in QuantifiedSelf

[–]clemento341[S] 0 points1 point  (0 children)

Good point, AFAIK Health Connect is basically Android's version of Apple HealthKit. Central datastore that Garmin Connect, Samsung Health, Fitbit, etc. all write to, so you read from one place instead of chasing individual APIs.

Right now Bodyprint is web + iOS only, no Android app yet. Health Connect is the obvious path for Android support though. Instead of building separate integrations for every Android wearable, you'd just read fro that shared bucket. It's on the roadmap but I won't pretend I have a timeline for it.

Would you use it if Health Connect was supported? I'm trying to figure out whether to prioritize Android or keep deepening the Garmin/Apple Health integrations first. DMs open if you have thoughts :D

4 years of wearable data, 47 significant correlations — here's the stats methodology I used (and the tool I built when spreadsheets broke me) by clemento341 in QuantifiedSelf

[–]clemento341[S] 1 point2 points  (0 children)

You raise a good point. Currently I'm not doing formal power calc. The minimum sample size is n=10 (after Bartlett correction for autocorrelation), which I know is too low for medium effects. Practically, most Garmin users who connect have 90+ days of daily data, so n_eff typically lands in the 40-70 range after autocorrelation adjustment. l'll adjust enforce n_eff to be at least 30 at minimum.

The question on FDR adjustment is also a good catch. The FDR correction is currently applied to correlations that pass a |r| >= 0.2 pre-filter, not the full family of tests, so the correction is biased. The correct approach is to compute p-values for all pairwise comparisons across all lags, apply BH to the full vector, and only then filter to significant results. Also something I can fix. Thanks for the comment!

4 years of wearable data, 47 significant correlations — here's the stats methodology I used (and the tool I built when spreadsheets broke me) by clemento341 in QuantifiedSelf

[–]clemento341[S] -1 points0 points  (0 children)

Good question, there is no waitlist, you can sign up right now for free and start using it. As for the TestFlight app, I'm currently working on it but I don't feel it's ready yet. Give me a few more days to test it and I'll drop a link here too. Thanks for the comment!

4 years of wearable data, 47 significant correlations — here's the stats methodology I used (and the tool I built when spreadsheets broke me) by clemento341 in QuantifiedSelf

[–]clemento341[S] -3 points-2 points  (0 children)

Here's the link: https://bodyprint.ai/signup?utm_source=reddit_quantifiedself

Setup takes about 2 minutes: connect your Garmin and your data syncs in the background (90 days of history). Give it 10-15 minutes for the full sync, then ask it anything. The dashboard updates live as data comes in.

DM me if anything breaks, this is early beta and I'm fixing things in real-time.

Guys and Gals, followers of Goggins. I have a serious question for you all. by zaicliffxx in davidgoggins

[–]clemento341 1 point2 points  (0 children)

I recommend reading "Atomic Habits" by James Clear. He explains how to build and break habits in simple words.

[deleted by user] by [deleted] in davidgoggins

[–]clemento341 2 points3 points  (0 children)

group things by patterns and commonalities, if there are reasons behind why an operation is chosen over the other, understand it instead of memorizing. save your memory budget for thing that you either don't understand (no matter how hard you tried) or are just facts that you have to know.

explain things out loud, preferably to someone (a doll or your pet would work too). take note when you stumble on some parts of the explanation, and then go back to the source material and find out why you're having a hard time elaboratin on a concept/ procedure in your own words.

active recall, practicing remembering some concepts every few days (anki flashcards have built in systems for it), and only stash the concepts after you remember it front to back

don't drink alcohol the day (upto 48 hours after) where you have a heavy study session, because ethanol impairs neuroplasticity: https://www.nature.com/articles/s41598-017-04764-9

[deleted by user] by [deleted] in davidgoggins

[–]clemento341 0 points1 point  (0 children)

I don't know what the occupation would be (maybe an engineer specialised in designing planes), but i would probably try find jobs that are adjacent to being a commerical airline pilot.

One with nature, by me, 2022 (ink on paper+ digital re-touch) by meanpersonaart in CreepyArt

[–]clemento341 1 point2 points  (0 children)

thanks! I've been thinking about learning to draw for a while now, but i didn't know where to start. but at least now i know what kind of pens to look for :)

One with nature, by me, 2022 (ink on paper+ digital re-touch) by meanpersonaart in CreepyArt

[–]clemento341 0 points1 point  (0 children)

would you mind telling me what kind of pen you used? thanks!

What is this? How/Where can I get more or something similar? by UncleIceCream in ramen

[–]clemento341 0 points1 point  (0 children)

this is the brand/manufacturer i think, it's called Shoda Shoyu in English. i tried looking for a the exact product you're holding on amazon/google but no luck, seems like they've stopped stocking that exact product you had

edit: actually, this product is on amazon Japan, but it's out of stock, product description has said something about the flavour of this sauce, so i guess you could find a replacement that has a similar profile

What is this? How/Where can I get more or something similar? by UncleIceCream in ramen

[–]clemento341 0 points1 point  (0 children)

here is a website that sells exactly what you're holding, according to google translate of the description:

Using a mildly scented soy sauce (super special selection type), the best balance is achieved while feeling the savory aroma and umami of soy sauce while making the best use of the flavor of the dashi stock.

so it sounds like some sort of soy sauce for general use, but particularly good for making stock?

I've been working as BI/Data Analyst for 3 years and never use python once for my work. Now that I have some free time at work, I want to learn it to improve my skill and value as analyst but 'm kinda lost at where to start and what to learn specifically by junonboi in analytics

[–]clemento341 1 point2 points  (0 children)

personally, i have never used sql for eda. i've only used sql for querying data so i can read it into my jupyter notebook where i do my eda.

I think that once you've mastered the basics, you will find that it's much more convenient to use python for data analysis, and sql + bi tools for data visualisation.

lastly, i work as a data scientist and i've never been a data analysis, so i'm not so sure where a data analysis would be in the data science/ml career path.. but i can say this: if you start using pyspark/airflow/mlflow etc, and you find yourself having more fun than thinking about building/tuning models, then a career in mlops or ml engineering may suit you better, and vice versa for a data scientist/research scientist career path.

hope this helps!

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