
r/QuantifiedSelf

Dad just died of a heart attack at 60. Heart Attacks took every man on his side. What can I do? 36M
I was always pretty healthy, studied exercise science and nutrition in college and was very active in the gym. I never was much for going to the doctors (literally 10 year gap). I'm 36, 2 babies and nowhere near as clean of a diet that I used to have nor time for the gym, and my dad's sudden death did lead to some increased drinking for the pain, I've been better at limiting the drinking as of late.
My dad (my best friend) recently dying out of nowhere at 60 was a huge wake up call. I thought since I was in shape heart disease wouldn't be an issue for me like it was my dad, grandpa, uncle and great grandpa but now I'm actually worried.
Almost all my biomarkers came back healthy... except my heart, go figure.
What do I do now to clean up my heart?
Thanks in advance for any suggestions.
Weekly Lifestyle Data and Analytics App Thread
Post your apps here, and please support people bringing unique ideas to this space.
60 logs over 6 weeks showed me my triggers were less random than I thought
I used to think this behavior was random. Sometimes it felt like a mood thing. Sometimes it felt like a willpower thing. Sometimes I did not even know what problem I was trying to solve.
So about 3 months ago I started logging the moment before the urge instead of the aftermath: time, location, emotion, energy level. That became the minimum dataset.
After around 60 entries, the pattern became obvious. The strongest cluster sat between 10pm and 1am. Most entries were not really “I want this.” They were boredom, fatigue, loneliness, or just the need for a fast mental escape.
What surprised me was that the data showed function, not just frequency. The behavior was consistently solving the same emotional state.
Seeing the entries visualized over time (I used a tracker called TONIX that turns logs into a heatmap) made the pattern impossible to ignore. What felt random in my head looked extremely consistent on a timeline.
Curious if anyone else has had a tiny dataset completely change how they understood a habit.
Most WHM research has focused on healthy people. The immune modulation findings from the 2014 PNAS study are compelling, but what about people dealing with active cancer and elevated chronic inflammation? That question hasn't been rigorously studied yet.
A team led by Sara Matijevic, PhD at Oxford is running a 16-week feasibility pilot to find out. Physician-gated, preregistered, fully open-access. Wim Hof himself is a special advisor on the study.
I'm sharing this because I work with ResearchHub Foundation, which is hosting the proposal, and this community felt like the right place to bring it. The study is peer-reviewed and open for community crowdfunding.
Checkout the full proposal here: https://www.researchhub.com/proposal/4459/researchhub-proposal-wim-hof-method-whm-cold-exposure-for-cancer-instructor-guided-citizen-pilot
have been tracking my Emotional States[Hedonic Cycle], Productivity, Sleep for over a year now, below is the Data for the Current month and the Experience.
The post is about my experience of tracking myself for over a year and I hope that I would help someone or get some new insights.
So i have Been tracking myself for a year now and i started because was i was extemely dysregulated. I was not able to function properly, I failed in my academics in multiple subjects, was not able to do any exercises, not able to socialize and was completely consumed by thoughts at a point that I couldn't control myself and then I brainstormed every idea that could save me and eventually I read a book 'The Now Habit'- Neil Fiore. Then I started tracking myself and it gradually became this detailed, now I am able to predict my future emotional states as I learned about Hedonic treadmill and by tracking my emotions everyday
As you can see in the images you can see my tracks which contains multiple things and it really helped me, currently I am able to function just enough to pass the academics and go on with daily life without any severe crashes. I read multiple books after completing the Now Habit and every one developed me and I believe that by tracking youi emotions and multiple things such as sleep, Hrv, etc. You can predict and control your crashes. The below is the Books I have read that were very helpful. 1] The Now Habit 2] Self Compassion 3] Tiny Habits 4] Staring at the sun 5] Authentic Happiness
there were many other books I read but these were particulary impactful so that's all and I would love to hear some insights. I would like to make a detailed post if someone requires it.
Built an app for 14 months that visualizes your WhatsApp communication patterns. Curious if this type of visualization resonates with you?
Hey r/quantifiedself, long time lurker here.
I’ve spent the last 14 months building an iOS app called Cirano that shows you the shape of how you actually communicate on WhatsApp. It pairs on-device, nothing leaves your phone. It surfaces things like who initiates more, how reply timing drifts, and which threads have quietly gone cold without you noticing.
There are five axes: Speed, Balance, Rhythm, Consistency, and Initiation. There’s also a small EKG-style line that acts like the heartbeat of a thread.
I’ve found it weirdly useful. For example, I realized I had basically stopped initiating with a close friend about three months before I would have consciously noticed it. But I honestly can’t tell if that’s just a me thing or something other people would actually want.
It just hit the App Store today, so I’m genuinely curious if any of this resonates. It feels like a different kind of app to me, something realtime and personal rather than another AI tool helping you do things faster. WhatsApp is only the beginning, thinking of additional chat apps and data streams.
Some interesting findings I noticed from working on an AI health agent.
Hey everyone! This isn’t an ad in any sense, just wanted to share a few findings. I’ve stopped promoting Oplin.app in this subreddit, but I still want to share some insights from what I’ve noticed while building it.
Quick heads-up: I used AI to improve the wording a bit, mostly for vocabulary and flow. The observations are from me but AI is just better at writing.😂
Here are some observations from talking to users and watching how people think about AI, health, habits, and wellbeing when using Oplin.
- Habits: One thing I’ve noticed is that people often say they want to build daily habits, but in practice they don’t consistently add or track them. The intention is there, but the daily friction is real. It’s a good reminder that “I want to do this every day” and “I will actually log this every day” are very different things.
- Privacy & AI: Another interesting contradiction is around AI and sensitive health features. People often ask for features that would require very deep personal context, sensitive health information, and in some cases even regulatory/FDA-level considerations. But at the same time, many of those same people are uncomfortable sharing that kind of information with AI. It feels like a version of the privacy paradox: people want the intelligence and personalization, but not always the data-sharing required to make it work well.
- Psychology: I’ve also seen that people genuinely like everyday conversations with AI. Not necessarily big “doctor replacement” use cases, but small check-ins, reflection, emotional support, motivation, and general psychological support. Some people seem to treat AI less like a tool and more like a daily companion or sounding board.
- Blood Reports: Another surprising pattern was around blood reports. I expected users to be hesitant about uploading blood tests and then clicking “Share this with AI.” Weirdly, it became one of the most used features: 360 out of 400 uploaded health documents were shared with AI. My guess is that either people don’t really understand their blood reports and find AI explanations genuinely useful, or blood tests don’t feel as “sensitive” to them as other types of health data.
Wearable inconsistencies: One weird trend I noticed was around sleep tracking differences between devices. Users kept bringing up inconsistencies between Oura and Garmin. In the cases I saw, Oura often showed less deep sleep, but still gave a higher overall sleep score. Garmin, on average, showed around 25% more deep sleep, roughly 20 extra minutes, but its sleep score was around 15% lower compared with Oura.
- Subjective & Workouts: Finally, workouts seem to have the strongest relationship with subjective “feel well” improvement compared with everything else I’ve seen. Obviously this is not a clinical claim, just an observation, but the difference was striking. When people worked out, they tended to report feeling better much more clearly than with other inputs. I know this is expected, but the percentage is still shocking (~97% reported feeling better the next day)
Overall, building in this space has made me realize that the hard part is not just AI or health data. It’s human behavior. People want personalization, but they also want privacy. They want habit change, but not friction. They want health insights, but not necessarily a medical product. And sometimes what they value most is simply having something there to talk to every day.
There are a lot more findings that I can share, so feel free to let me know if you find this interesting.
Does anyone else feel like recovery is becoming more “high-tech” lately?
"Maybe it’s just the content I’ve been seeing recently, but it feels like recovery and longevity are becoming way more advanced compared to even a few years ago.
PRP, peptides, red light therapy, regenerative treatments, recovery tracking, specialized clinics.
Some of it genuinely seems promising, but at the same time it’s hard to tell what’s actually backed by strong results versus what’s just packaged really well.
I’m not against any of it at all, I’m honestly just trying to understand where things realistically stand right now.
Curious how others here view this shift."
Built a tool that combines Oura/Strava/Garmin sleep + fitness data with finances, tasks, email, and calendar into one daily AI brief
For the past few months I've been building something I kept wishing existed: a single morning brief that synthesizes all the noise before I open any app.
Every morning it reads my Gmail, Google Calendar, Strava, Todoist, GitHub, and Plaid accounts, then writes a structured brief — schedule, health, finances, open tasks, email highlights.
The part I'm most excited about: you can annotate any item and the AI acts on it. Comment "draft a reply saying I'll be there" on an email thread → it drafts it. Comment "reschedule to Thursday" on a calendar event → it moves it.
These get handled during a Morning Review command while you're still drinking coffee.
There's also a personal wiki the AI reads and updates — notes on people, projects, open threads. Cross-linked, searchable, and actually used (not just sitting there like Notion).
Opening early access to a small group now. Happy to answer questions about how it works or what integrations are supported.
Here's a guide to better understand what actually influence staying asleep based on research. This is a completely different area than what influences sleep latency (falling asleep faster) and focuses mostly on Wake After Sleep Onset. I broke this into 5 main areas: nutrition, supplements, exercise, environment, and demographics. Hope you find it useful overall!
I added a plain english explanation column for each row and short definitions to start which I hope helps make it easier to understand each factor. All sources are linked at the bottom.
I know the tables are difficult on mobile so I take all the data to make it into a free tool that lets you explore the data in a more visually appealing way. Here's the page if interested: kygo.app/tools/staying-asleep-factors
Acronyms:
WASO - Wake After Sleep Onset
PSG - Polysomnography
SMD - Standardized Mean Difference
RCT - Randomized Controlled Trial
SWS - Slow Wave Sleep
AHI - Apnea-Hypopnea Index
SWSD - Shift Work Sleep Disorder
HPA - Hypothalamic-Pituitary-Adrenal (axis)
Nutrition
| Factor | Impact | Key Info (study/effect size) | Plain English | Evidence |
|---|---|---|---|---|
| Dietary Fiber | Decrease Arousals | St-Onge 2016; n=26, PSG, controlled crossover | More fiber = fewer nighttime wake-ups | Strong |
| Sugar / Refined Carbs | Increase Arousals | St-Onge 2016; n=26, PSG, significant predictor | Sugar directly increased sleep arousals | Strong |
| Caffeine | Increase WASO +12 min | Gardiner 2023; meta-analysis, 24 studies | Caffeine adds ~12 min of nighttime waking | Strong |
| Alcohol | Increase Fragmentation | Spadola 2019; Jackson Heart Study, n=785, actigraphy | Sleep breaks apart as alcohol metabolizes | Strong |
| Late Eating (<1hr) | Increase WASO 2–2.6× odds | Crispim 2022; British J Nutrition, large n | Eating right before bed doubles wake-ups | Moderate |
| Tart Cherry Juice | Decrease WASO ~17 min | Pigeon 2010; n=15, RCT crossover, insomnia cohort | Cherry juice cut nighttime waking vs placebo | Moderate |
Supplements
| Factor | Impact | Key Info (study/effect size) | Plain English | Evidence |
|---|---|---|---|---|
| Melatonin (immediate-release) | No significant WASO effect | Menczel Schrire 2022; meta-analysis of RCTs, Neuropsychopharmacology | Standard melatonin does not help you stay asleep | Strong |
| Ashwagandha (600mg/day) | Decrease WASO, SMD −0.39 | Cheah 2021; meta-analysis, 5 RCTs, n=400 (3 trials/281 for WASO) | Ashwagandha significantly reduced nighttime waking | Strong |
| Glycine (3g) | Decrease WASO, faster SWS onset | Yamadera 2007; n=11, PSG-measured, crossover | Glycine reduced waking and deepened sleep | Moderate |
| Magnesium (500mg) | Increase Sleep efficiency (elderly) | Abbasi 2012; RCT, n=46, 8-week, 65+ years | Improved efficiency but no direct WASO data | Limited |
| L-Theanine (200–450mg) | Mixed WASO results | Systematic review 2025; benefits at 200–450mg/day | Some maintenance benefit but inconsistent alone | Limited |
| Valerian Root | No consistent WASO benefit | Shinjyo 2020; meta-analysis, 60 studies, n=6894 | Subjective improvement only, no objective WASO change | Weak |
Exercise
| Factor | Impact | Key Info (study/effect size) | Plain English | Evidence |
|---|---|---|---|---|
| Moderate Aerobic Exercise | Decrease WASO ~10 min | Riedel 2024; meta-analysis of RCTs, insomnia patients | Regular cardio cuts ~10 min of nighttime waking | Strong |
| Resistance Training | Decrease Sleep disturbance, Increase efficiency | Kovacevic 2018; systematic review, 13 studies, n=652 | Strength training improved mid-sleep disturbance | Moderate |
| Yoga | Decrease WASO ~56 min | Bu 2025; network meta-analysis, 22 RCTs, n=1348 | Yoga showed large WASO reduction in insomnia patients | Low |
| Evening Moderate Exercise | Decrease WASO | Dolezal 2017; systematic review, 34 studies | Moderate evening exercise helps you stay asleep | Moderate |
| Vigorous Exercise ≤1hr Before Bed | Increase WASO risk | Stutz 2019; meta-analysis, 23 studies, Sports Medicine | Intense exercise right before bed may fragment sleep | Moderate |
Environment
| Factor | Impact | Key Info (study/effect size) | Plain English | Evidence |
|---|---|---|---|---|
| Bedroom Temp (20–25°C) | Decrease WASO at optimal range | Multiple studies; PSG-measured, 20–25°C optimal | Too hot or cold increases nighttime waking | Strong |
| Light at Night (even dim) | Increase WASO | Cho 2016; n=23, PSG, 5–10 lux, Chronobiology Int | Even dim light during sleep increases wake time | Strong |
| Noise (>50 dBA) | Increase WASO +30 min | Basner 2018; WHO systematic review, 74 studies | Noise above 50 dB adds ~30 min of waking | Strong |
| CO2 >1000 ppm (poor ventilation) | Increase Wake time +5 min | Kang 2024; n=36, field-lab, 3 ventilation levels | Stuffy bedroom air measurably fragments sleep | Moderate |
| Mattress (medium-firm) | Decrease Most consistent WASO | Hu 2025; n=12, PSG, 3 firmness levels compared | Medium-firm mattress gave most stable sleep | Limited |
Demographics
| Factor | Impact | Key Info (study/effect size) | Plain English | Evidence |
|---|---|---|---|---|
| Aging (30–60+) | Increase WASO ~10 min/decade | Ohayon 2004; meta-analysis, 65 studies, 3,577 subjects | Each decade adds ~10 min of nighttime waking | Strong |
| Female Sex | Paradox: more complaints, better PSG | Ohayon 2004; women better objective metrics, more subjective complaints | Women report worse sleep but objectively sleep better | Strong |
| Menopause (hot flashes) | Increase WASO, 69% flashes awakening | Joffe 2013; PSG + GnRH model, n=29, hot flashes = 27% of WASO | Nighttime hot flashes are a major wake trigger | Strong |
| Obesity (BMI ≥30) | Increase WASO significantly | Zhao 2021; Sleep Heart Health Study, n=5,723, PSG | Higher WASO independently associated with obesity | Strong |
| Shift Work | Increase WASO, Decrease sleep efficiency | Wickwire 2017; narrative review, SWSD patients, Chest | Shift workers have more fragmented daytime sleep | Moderate |
| Nocturia (≥2 episodes) | Increase WASO +34 min | Fung 2017; SOF study, n=1,520, actigraphy | More bathroom trips = much more nighttime waking | Strong |
| Obstructive Sleep Apnea | Increase WASO, Increase arousals with severity | Patel 2019; comprehensive review, PSG data | Each breathing event triggers arousal and waking | Strong |
| Chronic Pain | Increase WASO, large effect | Mathias 2018; meta-analysis, 37 studies, PSG | Pain significantly increases nighttime wake time | Strong |
| Psychological Stress | Increase WASO via cortisol elevation | Vgontzas 2001; n=24, 24-hr cortisol sampling, PSG | Stress hormones directly fragment sleep | Moderate |
Sources
I got tired of the WHOOP app's limited features , so I built my own Health Dashboard
I’ve been a WHOOP member since Dec 2025, and while I love the data, the app’s 6-month window and limited chart views always frustrated me. I wanted to see my recovery trends over the long term and compare them directly with my Apple Watch running stats without jumping between apps.
So, I built my own private dashboard (PWA).
• Proper Heatmaps: I finally have a GitHub-style view of my recoveries for the whole year. It makes it so obvious when I’m overtraining or when that weekend caught up with me.
• Custom Date Ranges: No more "Week/Month/6 Month" limits. I can look at any specific block of time (like my 10k training block) and see exactly how my HRV responded.
• Best Efforts: Since it pulls my raw .fit files from Apple, it calculates my PRs for 1k, 5k, etc., automatically. The official app never felt "smart" enough for my runs, so I fixed it.
• Total Ownership: I host this on a small machine at home. No subscriptions, no cloud—just my data sitting in my own database where I can actually use it.
At what point do you stop researching and actually try something?
I’ve spent months reading threads, studies, podcasts, and personal experiences about different regenerative and recovery treatments, and honestly I still haven’t fully committed to trying anything.
Part of it is the cost obviously, but I think the bigger issue is how wildly different people’s experiences are. One person describes life-changing results and another says it barely helped.
After a while it feels like too much research almost makes the decision harder instead of easier.
Curious if anyone else here has reached that point where more information stopped helping and you just had to decide for yourself.
Weekly Lifestyle Data and Analytics App Thread
Post your apps here, and please support people bringing unique ideas to this space.
Measure all the things!
Just finished a light chest workout! Thinking what can I do with all this information!
Fitbit to become Google Health
I haven't seen this posted here, but on May 19, 2026 the Fitbit App will officially become the Google Health App. While the Fitbit trackers will remain, the app will be completely redesigned and features will be eliminated. Just to list some of the features being discontinued:
- Fitbit Sense and Versa 3 users only: Snore Detection will no longer be available.
- Estimated Oxygen Variation (EOV) will no longer be available.
- Sleep Profile will no longer be available.
- Skin Temperature minute-by-minute data will no longer be available.
- Blood glucose tracking will no longer allow you to add symptoms or remind you to check your levels.
- Setting calorie targets with "Food Plans" will no longer be supported.
- Resilience replaces Stress score. And will now be described as Optimal, Balanced, or Low, instead of a numerical value.
- You can’t choose a unique username or custom photo.
- You will no longer be able to send and receive direct messages and notifications from others.
- The Groups and Community Feed features are removed.
- Badges will no longer be supported. New badges won’t be generated, and your historical badges will be deleted.
- Connections to Lifescan devices will no longer be supported. However, you can still manually log your Glucose data.
- Data associated with features that are being removed will be available to download or delete until July 15th, at which time we’ll begin deleting the data from our systems.
Link to the full list of feature being removed: https://support.google.com/fitbit/answer/17068213?utm_source=transactional&utm_medium=email_crm&utm_campaign=GS206280&utm_term=fitbitcrm&utm_content=bodylink1&hl=en#zippy=%2Cfitness%2Csleep%2Chealth-wellness%2Csocial%2Cbadges-celebrations%2Cthird-party-connections
I personally have been wearing a Fitbit device since December 2014. Fitbit (and Tim Ferriss's book, The Four Hour Body) was my jumping off point into the quantified self life. I used to just simply wear my Fitbit, note how many steps I got and move on. Eventually, I started exporting the data from Fitbit into Excel files every couple of months. Then more frequently. My system was nearly destroyed when Google took over and changed the data export feature. I began documenting my data daily instead of monthly because I literally couldn't get my data out of the Fitbit app anymore.
Now in 2026, I still wear a Fitbit Charge 6 (my second version of the same device because they haven't made any new hardware releases in that line since 2023). I only rely on it to capture my daily steps and my sleep. Fitbit can't be used for anything else. Thankfully, those two features seem to be remaining, but I can imagine that other apps that use the API from Fitbit or Google Health Connect will see an impact when Fitbit is ended.
Irregular sleep tracking auto wake-up time
My sleep schedule is very irregular, might be as early as 9pm or late 2am. I am looking for a tool/tracker that detect my sleep when it reach 7 hours to wake me up, preferably by turning a light to wake up naturally.
Sleeping is a daily task and Fitbit/Garmin, all that I have seen, you have to wake up open your eyes to see how many hours was sleep. And it is very disrupting to sleep and not match for this use case.
Any ideas or recommendation for trackers that facilitate this waking up signal that adjust automatically??
What would make a $500 brain stim device earn a spot in your stack?
Ok so I'm genuinely trying to figure this out before I spend the money.
My situation is pretty simple. I work a screen heavy desk job. 8 to 10 hours of calls, docs, slack, emails, the whole thing. By 6pm I am COOKED. Not sleepy. Not tired exactly. Just..... fried. Like my brain used up everything it had and there's nothing left for the rest of my life. I can't make decisions. I can't cook dinner. I can't have a real conversation with my girlfriend without zoning out. I just sit there scrolling or ordering takeout because choosing what to eat feels like too much. Every single night.
I've got the passive tracking stuff down. Oura for HRV and sleep. The data is clear, my stress is elevated and my recovery is trash. But KNOWING that hasn't changed anything. I have 14 months of data confirming I'm burnt out every evening. Cool.
Tried the low hanging fruit. Brain.fm for focus music. NSDR on youtube. Breathwork apps. Caffeine cutoff at 1pm. All of it helps a LITTLE but nothing has touched the evening crash. That's the thing I actually want to fix.
So now I'm looking at active neuromodulation. Specifically tDCS. The research on prefrontal cortex stimulation for stress regulation and cognitive fatigue is interesting.... 1-2 mA, 20 min sessions, been studied for decades. DIY kits like NeuroMyst or Caputron are cheaper but honestly I don't trust myself with electrode placement and saline prep every morning. The one I keep coming back to is Mave headset because it's preset placement, no subscription, and specifically positioned around stress and focus. Not depression not clinical stuff. Just..... daily brain maintenance I guess.
My plan if I buy it: 2 weeks baseline tracking what I already do. 4 weeks of daily use. 1 week washout where I stop completely. If things don't regress during washout then it was probably just placebo or me being more careful with sleep and caffeine that month.
What I'd actually be measuring: Can I cook dinner without feeling like a zombie. Am I ordering less takeout. Does my girlfriend stop asking me "are you ok" every night. Oura HRV and sleep latency as a safety check.
My threshold: if the evening crash doesn't meaningfully improve after 4 weeks I'm returning it. I don't care how cool the tech is. Does it actually help me have a life after 6pm or not.
What would YOUR threshold be before adding something like this to your stack? Published data? Transparent protocols? Or just "did it actually make my evenings better"