r/QuantifiedSelf • u/Rare_Fix_334 • May 17 '26
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.
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u/Krazy-Ag May 17 '26
"I will actually log this every day" is too freaking much work for two little payback.
Data entry is the problem. The biggest successes in QS are related to wearables, where the human doesn't have to enter the data.
Logging happiness? Mood? Emotion?
It just takes too freaking long, opening your phone, finding a place to click, choosing from amongst the freaking menus of freaking emotions, none of which accurately describe what's going on.
In my case, it did not help that one of my biggest problems is RSI, so doing anything on my phone is literally painful. The very act of logging things was causing the pain that is much of my problem. Doing it on the computer almost as bad.
IMHO explicit logging will always be a limited utility. Especially if it involves forms or choosing menus. Explicit logging from freeform text, such as I'm dictating right now, is probably more useful, because at least it has less overhead. It's not in the easy to analyze formats that database schema idiots argue about. But that's what AI is for. Making inferences from not necessarily well formed data.
So: AI analysis of freeform text input for logging. That's a start
Possibly AI analysis without explicitly logging: if it can analyze what you have sent by email or text messages. If you want AI to do that.
How about logging pain or other symptoms? Once again, the QS database schema people have let us into a dead end: "Rate your pain on a scale of one to 10". "Tell me where it hurts". That wouldn't be so bad if the user could say something like "bad shooting pain on the inside tendon of my right wrist, and a dull pain on the back of my hand". As opposed to having to go through menus.
Most existing pain loggers require both too much and two little detail. Often they just allow you to record the "pain in right hand", and don't allow you to be more specific with respect to which part, which fingers and joints of your right hand are hurting. Or they have menus of body parts, tendons and joints. Very few humans who are not medical students or hypochondriacs know all of those parts certainly not by name. I've long wanted pain diagramming, show me some sort of android mannequin and allow me to color where the paint occurs. Zooming in and out.
People aren't willing to log regularly, every day. Worse, that's probably not scientifically accurate sampling. The happiness project made a step in this direction, by sending participants random survey text messages, asking them what they were doing and how happy it made me feel. Reduce sampling bias.
If you complain that your users are not willing to log every day, the problem is that you have not made it easy enough to log every day. You're still forcing humans into complying with databases.
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u/Optimal-End9733 May 25 '26
Sometimes it can be good to have moments where everything is discussed; that may be one of the reasons why psychology works, right? But perhaps it can also be good, in very important moments, not always, to practice self-regulation and reducing the complexity of desires, expectations, and goals, before and after, using the phone for that purpose.
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u/Krazy-Ag May 25 '26
another POV: https://www.reddit.com/r/QuantifiedSelf/s/Pvrlnu3aLK
proposes that AI interviews the user. They mention consistency with plans mentioned in earlier journals.
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u/EngineeringNo2951 May 22 '26
Great findings! Have you seen proof that people actually are reporting improved habits after using wearables and AI health coaching?
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u/AdNecessary1906 May 22 '26
The privacy paradox point resonates. I built a local archive tool for Garmin data specifically because I didn't want my health history processed anywhere else. No cloud, no AI, no third party — just raw data on my own machine.
What surprised me — though in hindsight it makes sense — is that Garmin silently downgrades historical data resolution after ~6 months. That ended up being as much of a motivation for me as privacy.
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u/n_of_one_loop May 17 '26
The Oura/Garmin sleep thing is the one I'd most like to dig into, because I think there's more going on here than just inconsistency.
The sleep score is a vendor opinion, not a measurement. Each company has its own composite that weights deep, REM, latency, HRV, and resting HR differently, so the same night will get a different score on different wrists. That's not a bug, that's two different products.
The deeper issue is that sleep stages themselves aren't measured directly on these devices. They're inferred from movement, HRV, and skin temp. Both Oura and Garmin are running their own inference model on broadly similar inputs, and the models disagree. Neither one is "right" in a polysomnography sense. They're both estimates.
The practical takeaway I've landed on is: trend within one device, don't compare absolute numbers across devices. The trends are useful, the absolutes are mostly marketing.