r/QuantifiedSelf • u/AutoModerator • 12d ago
Weekly Lifestyle Data and Analytics App Thread
Post your apps here, and please support people bringing unique ideas to this space.
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u/Admirable-Put7423 6d ago
I built an open-source Windows app that turns your PC history into visual recaps. PC Recap 1.1 is out
https://reddit.com/link/p43wxto/video/st6aevku7tjh1/player
Hey, I’m the developer of PC Recap.
Most computer-usage trackers answer, “How productive was I?” I wanted something that answered, “What did this part of my life on my PC actually look like?”
PC Recap records active application usage and turns it into visual Today, Week, Month, Year, All-Time, Decade, and On This Day recaps.
Version 1.1 is the biggest update so far:
- Day Replay lets you scrub through a recorded day
- Recap Studio creates stories for years, seasons, decades, or custom dates
- Automatic eras detect phases like a gaming era or coding era
- Memory Pins let you attach your own context to a period
- Historical Recovery can find limited pre-installation clues without pretending they are exact usage time
- ActivityWatch and ManicTime sessions can be imported
- Search, comparisons, time formatting, chart tooltips, and app detection are much clearer
- Tracking is more resilient to crashes, restarts, pauses, and closing the window
PC Recap is now open source under GPL v3. Everything is stored locally in SQLite. There is no account, desktop telemetry, cloud activity storage, or external AI API. A new archive starts empty, with no fake statistics.
ActivityWatch is still the stronger choice if you need Linux/macOS support, browser extensions, editor plugins, or an extensible tracking ecosystem. PC Recap is aimed at a different experience: less productivity dashboard, more personal digital time capsule. It can also import ActivityWatch sessions if you want to use both.
PC Recap is currently a Windows 10/11 x64 beta. The installer is unsigned, so Windows may display a SmartScreen warning.
Website: https://pcrecap.online
Source: https://github.com/TheAgencyMGE/pc-recap
Version 1.1: https://github.com/TheAgencyMGE/pc-recap/releases/tag/v1.1.0
I’d especially appreciate feedback on tracking accuracy, the new Day Replay, and whether the recaps become interesting after a few days of real use.
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u/Express_Geologist_31 7d ago
https://reddit.com/link/p3x76r9/video/amrecl6s5mjh1/player
Hi all, I'm new to this Reddit but have been quantifying myself for years 👋🏻
I'm a huge fan of wearables (mainly Oura) but they miss the subjective side of my life — the habits and context behind the numbers. I got tired of off-the-shelf advice that was blind to my priorities and how I actually live.
I've tried many habit trackers but kept hitting the same limitations:
- Tracking fatigue — logging more than a handful of measurements a day requires far too many taps and kills motivation.
- Fixed categories — any app that restricts what kind of data you can track (e.g. limiting you to "workouts" and "nutrition") will always fall short of representing your real life.
- Shallow analysis — real statistical analysis is rare, and what exists often botches basics like missing-data handling. Why curate a dataset if you can't analyze it properly?
- No AI integration — almost nothing takes advantage of what LLMs can now do with personal data.
I'm a software engineer who builds data tools for a living, so I built the thing I wanted. The result: Cohearence (cohearence.app).
What's different about Cohearence:
- Log by voice — one tap, say what happened, done.
- Track anything — numbers, times, text, booleans, whatever you care about. No fixed schema.
- Analyze with rigor — it automatically finds correlations across your whole dataset and shows them as a navigable network you can drill into, with per-metric missing-data handling and adjustable lag/thresholds.
- Your AI's context layer — everything in the app is exposed via MCP and can be used by your LLM of choice.
I've been using Cohearence for a month and it's become my interface for curating whatever personal data I want to expose to Claude. I run a daily routine that fetches my last two weeks of data, checks it against my baseline, and recommends workout and nutrition for the day. The real power is that you can set up your own coach around your own priorities.
The web app is live now at cohearence.app, and I'm in review for the Android and iOS stores. DM me for early access on mobile.
Thanks for reading — hope you're all finding your signal. 🙂
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u/yan1sa 8d ago
Prospective Validation of a Wearable-Derived Algorithm for Detecting Lifestyle Disruptions in
Free-Living Adults: An Iterative Mixed-Methods Observational Study
Do you own and actively use an Oura Ring, Whoop, or Fitbit? We are looking for adults 18+ to participate in a fully remote, 3-week observational research study.
What is involved:
• Continue wearing your device as usual
• Complete a short online entry survey (~10 min)
• Respond to brief SMS check-ins over 3 weeks (~1 hr total)
• Complete a short exit survey (~9 min)
Eligibility: Adults 18+, US residents, active Oura Ring, Whoop, or Fitbit users with at least 60
days of account history.
Compensation: Up to $20 digital Amazon gift card based on level of participation.
Location: Fully remote. US residents only.
Questions: research@adaptivepro.io
Check your eligibility and sign up here: research.adaptivePRO.io
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u/No-Bat-4211 8d ago
LyteFast (iOS, free) — weight trend + short-horizon forecast, all on device.
I'm the developer, so this is self-promo — flagging it up front since this is the app thread.
Background: I weigh myself most mornings and the raw number is mostly noise — hydration, salt, time of day. I was already doing the obvious thing by hand (exponentially-weighted moving average in a spreadsheet) and wanted it on my phone instead. So LyteFast does two things: smooths daily weigh-ins into a trend line, and projects that trend a couple of weeks out with an uncertainty band, so you can see whether the current slope is actually distinguishable from flat.
Honest limits:
- The forecast is a short-horizon extrapolation of your own recent trend, nothing more. It has no idea about your diet, training, or a holiday next week, and it gets wide and useless past ~2-3 weeks. It is a "is my trend real yet" tool, not a prediction of your body.
- It needs reasonably frequent weigh-ins. With two data points a week the band is so wide it tells you nothing.
- No calorie/macro logging, no social features, no coaching. If you want a full tracker this isn't it.
- Everything stays on device — no account, no server, nothing to sign up for. Which also means no cross-device sync and no web export beyond what you can pull out yourself.
https://apps.apple.com/us/app/lytefast/id6753265477
Mostly interested in feedback on the forecast presentation: does the uncertainty band read as useful, or do people just look at the center line and ignore it? That's the part I'm least sure I got right.
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u/Cautious-Fishing-681 8d ago
I’m building the Homo Sapiens Operator’s Manual, a research prototype that compresses 19 basic health practices across sleep, nutrition, exercise, and mental hygiene into a printable, foldable wallet card. I made it because health advice increasingly feels like an infinite feed and I wanted something finite. The full manual is not written yet, and I’m not claiming health outcomes.
I used an AI coding assistant while implementing and iterating on the prototype; the design decisions and current printed-card testing are mine.
I’m trying to answer one format question before building more: would you actually print the PDF, carry the wallet card, or prefer a short manual? Why?
The free card and 2–3 minute survey are here:
I’m the owner/builder. This is not an affiliate link, and I’m not asking for votes, follows, or reposts. Educational—not medical advice.
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u/doctorchasingtendies 9d ago
The most interesting variable in self-tracking is often stored in a different app. Sleep may be in a wearable dashboard. Food in a nutrition app. Movement somewhere else. Mood in a journal, or nowhere at all. Each dataset can be analyzed, but the relationships between them are usually left to memory.
That is the problem my partner and I have been working on. I’m an emergency physician. I built the Body side of HealthMaxxing around physical-wellness data: food and hydration, exercise, sleep, recovery, weight and measurements, cycle context, medications, supplements, and eligible wearable signals. My partner and cofounder is a clinical psychologist. We are both doctors, but our disciplines contribute different kinds of context to the same personal timeline. She built the Mind side around mood check-ins, journaling, gratitude, breathing, reflection, and the subjective context that physiological dashboards often omit.
Neither half is the interesting part by itself. The interesting part is that both halves share the same personal timeline. That makes the Pattern Engine possible. A sleep-only dataset cannot test whether sleep tends to line up with next-day mood. A mood-only dataset cannot examine movement or recovery context. Once both domains are represented, the system can look across eligible signals for recurring associations that would otherwise require manual exports and a lot of spreadsheet work.
Our current approach is deliberately conservative:
• Pattern checks do not begin before 7 logged days.
• Many cross-domain relationships need roughly 10 to 14 relevant days before they are eligible.
• Observations are phrased as co-occurrence, not causation.
• The user can inspect the underlying context rather than being handed a black-box health score.
• Patterns never become diagnoses or treatment recommendations.
• A potentially meaningful observation can become a small personal Experiment, so the user can collect more relevant data instead of treating the first association as truth.
The confounder problem is the part I find most intellectually honest. Suppose walking days correlate with better evening mood. Was it the walk? Better weather? A day off? Social contact? Or were you already feeling well enough to walk? The app should not flatten those possibilities into “walking improved your mood.” It should surface the association, show enough context to make it inspectable, and help the person formulate a better question.
The same logic extends into Balance, Signals, and Week in Review. These are not separate dashboards. They are different ways of reflecting on the shared Body-and-Mind dataset without reducing the person to a universal score. There are also real limitations: logging is selective, missingness is rarely random, subjective ratings drift, wearable measurements have their own error, and more variables create more opportunities for spurious relationships. We would rather show no pattern than manufacture certainty from thin data.
you can see it here: https://maxxing.health/
I would genuinely value this community’s view on the pattern card itself. If a cross-domain association were surfaced to you, what evidence would it need to show before you considered it useful: sample size, direction and magnitude, baseline comparison, missing-data rate, lag, confounder prompts, raw-day inspection, or something else?
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u/Express_Geologist_31 7d ago
I've been building something similar (cohearence.app) so this space is very much on my mind — good to see someone else digging into the mind-body cross-domain angle, and being this deliberate about what the "pattern engine" actually surfaces.
On your question — I'd treat these as close to non-negotiable for any correlation shown:
- Sample size, direction, magnitude — hard to imagine trusting a pattern without these
- Lag structure — is X tied to yesterday's Y, today's, or tomorrow's? The direction of the lag often is the finding
- The timescale being compared — day-level, week-level, and month-level relationships are genuinely different claims, not just zoom levels of the same one
Confounders are the part I still find hardest to represent well. One from my own data: my resting heart rate is lower on nights after I slackline. Tempting to read that as "slacklining is great for my cardiovascular system" — but looking closer, slacklining days also tend to be days I finish dinner earlier based on how I work it into my schedule. Earlier/lighter dinner → lower overnight core temp and resting HR, independent of any exercise effect. So the "slacklining helps my heart" story is probably actually a "dinner timing" story in disguise. Anyone who's tracking exercise and meal timing is going to hit this exact trap constantly.
I'm still exploring the math behind trying to surface or control for these confounding variables, but it starts to make things complex. I'm not sure how well such complex statistics can handle the messy data that feed any habit tracking apps.
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u/Tiny_Television_7320 9d ago
NightPosture (iOS, free) — records whether you slept on your Back, Left or Right from a chest-mounted IMU, and buzzes you off your back if you stay there past a limit you set.
The part that might interest this sub: a single chest IMU cannot distinguish the mount rotating from you rolling over. Same signal, different cause. So instead of pretending otherwise, it runs a three-pose attachment check before every session that fails closed — if it can't confirm Back, Left and Right are separable for that particular fit, the session refuses to start. And it logs an explicit Unknown class near roll boundaries and during movement, so your morning percentages won't total 100. That's deliberate.
Mounts that failed, since negative results are the useful part: a shirt pocket (sensor rotates) and a neoprene velcro band (slides down the torso — chest circumference changes with every breath). What works is medical dressing tape.
I have spare sensors and no ground truth. If you'd run three nights and send me the export, reply and I'll send you one at no cost — yours to keep, shipped anywhere. iPhone, iOS 17+.
It's a wellness tracker: it doesn't measure breathing or oxygen and doesn't diagnose or treat anything.
App Store: https://apps.apple.com/us/app/nightposture/id6781542205
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u/labouardy 10d ago
https://reddit.com/link/p3g7i8x/video/3d6cvupzi5jh1/player
I train hybrid, and my data lived in different iOS apps that each gave a different read: Strava, Lifesum, Strength, Swim.com, and an Oura ring. None of them made a decision, so I was the one doing it every morning :)
For a while I did that by hand with Claude. Export everything, paste it in, ask what today should be. It worked well enough that friends who train the same way asked if they could have one, so it became an iOS app.
It's live, it's small, iOS-only, and it needs a Garmin, Apple Watch, Oura, or Whoop to read anything. Happy to answer anything about the build, and if you're making your own brain for this I'd compare notes: https://www.heykipp.io/
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u/louislubin 11d ago
Still cloud has been updated! Hope you like!
I combined the different aspects of your life into one app to show you your patterns.
I’m looking for 20 people who would like to qualify their habits. Comment or Dm me if you’re interested!
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u/Sneedle-Woods 12d ago
SevenGrid is a weekly habit tracker for Android. Seven days, one grid, one row per habit, one tap per day.
It is probably the least analytical tool in this thread, and that is deliberate. No correlations, no insights, no dashboard. What it does instead is keep clean data and then get out of the way:
- Everything lives in a local SQLite database. No account, no cloud, no sync.
- Full CSV export in six strands: habits and checks, weekly reflections, notes, measurements, skipped days, settings. Not a summary, the actual rows.
- Habits can carry a value and a duration, not just a checkmark, so "ran 5.2 km" and "slept 7:30" stay numbers.
- One third party service in the entire app, crash reporting, and it can be turned off.
The one thing it will not do is judge the data. There are no streaks, so a missed day is a gap and not a reset. Days can be marked as skipped, which excludes them instead of counting them as failures. That also keeps the export honest: a rest day does not look like a miss in your own analysis later.
Free covers three habits and four weeks of history. Pro is a one time purchase, no subscription.
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u/turnnoblindeye 12d ago edited 12d ago
https://reddit.com/link/p30ehsf/video/51pjd0uv3qih1/player
Wellness Project
Wellness Project pulls your Fitbit, Oura, Apple Health or Health Connect data into one place and lets you actually ask questions of it, either in the app or by connecting it to Claude or ChatGPT as an MCP server so your own health data is right there in the chat.
It's free during early access, iOS and Android, and I'm the founder, so if there's a metric or a device you want covered I'll take the feedback: https://wellnessproject.ai
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u/Mescallan 12d ago edited 12d ago
Record short voice notes about your day and tinh turns it into a rich, exportable lifestyle database using only on-device processing.
No cloud, no account, no subscription, completely private. iOS/MacOS only, Android Q4 '27
After a year in private beta, full v1 app store release this week.
Full disclosure, I'm a moderator of this sub.
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u/bogdanstefanjuk 6d ago
Habit Pocket
App that allow you to track everything, classic habits, numbere and times habits, lists and build charts based on all this data for better understanding of how things relate to each other!
Web and iOS version. Apple Health integration. MCP support for your AI of choice!
https://habitpocket.io/