Why my heart rate is on the internet

I built an ETL pipeline [1] for my personal health data. It updates every ten minutes and feeds a data warehouse [2]. And then I made it public… which might confuse you. It looks like a quantified‑self stunt [3] or some kind of cry for help, but it isn’t. It’s a privacy experiment, and the origin of it is much more mundane than it appears. It started with an unhealthy relationship with optimization.

A few years ago, I wanted to “improve” my body. Nothing dramatic, just a few centimeters here and there. I’m naturally skinny, so I focused on recomposition. I tried a ketogenic diet because some of the claims around it were interesting. I started counting calories and macros. The diet failed and I stopped, but the counting didn’t. At some point, calorie counting stopped being a tool and became a background process… a tab I couldn’t close.

It wasn’t the usual “this is too many calories” kind of thinking. It was more like “if I drink this macchiato now, the macros shift, and then I can’t hit 120g protein without overshooting my buffered BMR [4].” It had nothing to do with body image. It had everything to do with keeping the model coherent. From the outside, my eating habits looked rigid or odd or pathological. Easy to misinterpret as a “real” eating disorder. But the internal logic was different. No fear, no shame… just a kind of control that becomes cognitively exhausting, sometimes to the point of losing appetite entirely.

There isn’t a neat diagnostic box for this. Orthorexia nervosa [5] comes closest, but it’s only a proposed disorder. There’s no treatment protocol for “my brain turned nutrition into a constraint‑satisfaction problem [6].” Trying to explain this to you and others is tiring, so I started thinking about how to show instead of explain.

If you were going to make assumptions about my eating habits anyway, I might as well give you the data (and more)!

Building homeostat

So I built homeostat, a name I chose long before I learned it was also the name of a 1950s cybernetic device simulating a living organism. The resonance is accidental, but fitting. My homeostat collects vitals, nutrition, exercise, sleep, intraday metrics, and a 90‑day history window. And I publish it live on my website.

Not because I think it’s safe or normal, but because I want to interrogate what “private” actually means. I’m letting you see a lot about me… but you will still know very little about me.

Telemetry is not identity

When my routine is stable, my eating is repetitive and boring. Quinoa, brown rice, salmon, chicken breast, mixed vegetables, princess beans, oatmeal, yoghurt, berries, and some baseline supplements like iron because I avoid red meat. On off‑days I follow simple rules: no refined sugar, no red meat, avoid trans fats, go easy on salt. It’s nutritious, predictable, and completely unmarketable. There’s nothing to infer about my consumer identity. No brand affinities. No lifestyle aesthetic.

You’ll see that I’m active. You’ll see that my sleep is (hopefully) regulated. If it isn’t, you’ll know something is off. And here comes the irony: we talk about wanting society to be more open about having problems, but we recoil when someone actually exposes the telemetry [7] of their life… and at the same time, we’ve drifted into a cultural moment where personal identity is often treated as something that can be declared into existence without reference to any observable signal. I’m not trying to single anyone out here, it’s simply the broader pattern that concerns me. When interpretation becomes more authoritative than evidence, when narrative outruns observation, we lose the empirical layer that keeps us connected through a shared reality. I want to push in the opposite direction. I’m showing you the raw signals without the story, and watching which stories you decide to attach to them.

The privacy paradox

I’m a private person. I don’t like pictures of me being taken, I’m not on social media, and my digital life is intentionally low‑entropy. And yet I’m publishing my heart rate… This contradiction is the point. Privacy isn’t about hiding everything; it’s about choosing what matters. I want to decide what happens to images of me, and I want to decide in which context my words appear. I want to decide how and with whom I interact, and what parts of myself I make visible.

Health data might feel intimate to you. To me, it’s just raw signal… the kind of background noise a living system emits as it moves through time: Telemetry. But telemetry is not the same as meaning. A heart rate is telemetry. A diagnosis is an interpretation of telemetry. One is a measurement; the other is a judgment call.

A diagnosis carries assumptions, context, and value. It says something about you. It implies a narrative. It can follow you, shape how others treat you, and become part of your identity whether you want it to or not. Telemetry doesn’t do that. Telemetry is inert until someone decides what it “means”. And that is where danger enters.

Interpretation and projection

The danger isn’t in the numbers. It’s in the stories you might attach to the numbers. When you interpret data, you don’t just analyze it… You expose your own assumptions, fears, biases, and narratives. You reveal far more about yourself than about the person the data supposedly describes.

This is the same cognitive mechanism that makes identity‑based thinking so volatile. People stop seeing individuals and start seeing categories. They stop seeing context and start seeing symbols. They stop interpreting data and start interpreting identity. Once that shift happens, the person disappears behind the projection.

Health telemetry is especially vulnerable to this. A heart rate becomes “anxiety”, a calorie count becomes “restriction”,a sleep pattern becomes “instability”, a body shape becomes “evidence”. None of these interpretations come from the data. They come from the interpreter.

If someone sees me in real life and thinks “she’s skinny,” that’s already an interpretation. Publishing the data doesn’t create the judgment… it just makes the judgment visible. By exposing my telemetry, I’m not exposing myself. I’m exposing the interpretive machinery you carry. And that’s the part worth interrogating.

Conclusion

I’m not publishing my telemetry because I think it’s harmless. I’m publishing it because I want to understand what harm in this context actually means… and because I want you to notice the stories you tell when you look at someone else’s data. It’s not meant to be a wellness dashboard, it’s more of a mirror. And the reflection you see says something about you, not me…

Footnotes

[1]

ETL pipeline: Short for Extract, Transform, Load. A process used in data engineering to pull information from various sources, clean or reshape it, and store it in a structured system. In this case, it refers to the automated flow that collects and processes my health telemetry.

[2]

Data warehouse: A centralized storage system designed for structured data, optimized for querying and analysis rather than day‑to‑day operations. Think of it as a long‑term memory for datasets that need to be examined, compared, or visualized over time.

[3]

Quantified self: A movement centered around tracking personal data such as steps, sleep, heart rate, productivity metrics, or diet, often with the goal of self‑optimization. The term “quantified‑self stunt” refers to the more performative side of this culture, where data collection becomes a public display rather than a private tool.

[4]

BMR (Basal Metabolic Rate): The amount of energy your body needs to maintain basic physiological functions at rest, such as breathing, circulation, and cellular repair. Often used as a baseline for estimating daily caloric needs. A buffered BMR is the baseline including additional energy required for day-to-day activities (on average).

[5]

Orthorexia nervosa: A proposed eating‑related condition characterized by an obsessive focus on “healthy” or “pure” eating. Unlike other eating disorders, the concern is not weight or body image but the perceived quality of food. It is not an officially recognized diagnosis in major medical classification systems.

[6]

Constraint‑satisfaction problem: A concept from computer science and mathematics describing a system where multiple conditions must be met simultaneously. Useful for understanding how a simple habit like calorie counting can evolve into a rigid internal model that demands coherence.

[7]

Telemetry: Raw, continuous measurements emitted by a system. In this context, physiological signals such as heart rate, sleep duration, or step count. Telemetry is descriptive, not interpretive; meaning is added later by observers.

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