The Correlation Project

As a coder, concentration and mental focus become very important. Writing code is a very brain-intense activity which makes daily fluctuations of mental focus very obvious.

I already noticed this while studying medicine. Luckily, studying medicine is not an activity which requires a lot of brain activity (it’s more about being organised). When I started working as a software engineer however, this became more important.

Side note: There’s a talk

I gave a talk about this at ClojuTRE 2018 in which I summarized the whole concept and my findings in 20 minutes. Of course, there wasn’t enough time to talk about everything – that’s what this post is for.

Medical Considerations

On a practical level, I wanted to be able to rate my mental focus quickly and in a way which reflects how I feel when I leave the house in the morning. At this time I usually get a good feeling for whether it’s going to be a good or bad day concentration-wise.

Thinking a bit more about this, I came up with the idea of rating Brain Fog which for me is more or less the opposite of mental focus. Chris and I had done something very similar for masturbation already (long story) in which we wanted to correlate masturbation habits with mood, energy and libido in the days thereafter. At the time we decided to make users enter that data on a scale of 1 to 5 which proved quite usable. So I decided to adapt this slightly this time and choose a scale of 0 to 5 for “subjective Brain Fog”.

Apart from that, I didn’t put much work into medical considerations. That probably wasn’t very wise. I did however consider coeliac disease as a potential culprit even though I thought it would be unlikely in my case. My data however should be sufficient enough to prove this, right?

Engineering Considerations

This being a side project and me being a software engineer, I had the obvious tendency to overengineer everything to be able to try out all new technologies at the same time. Let’s write a backend in Clojure! Perfect opportunity to try out GraphQL! Let’s make the frontend a progressive web app with offline-first capabilities!

Well, that didn’t happen. Turns out I have a full-time job at Merantix and need to take care of some other aspects of my life in my spare time (sleeping, eating, brushing teeth).

Reflecting upon this, it became clear that I didn’t even know the structure of my data yet: What sort of data is a meal? What about an activity like going to the gym? How do I measure my mental focus? On which scale? How often?

As a software engineer, this is a great opportunity to put your engineering pride behind you and use Google Sheets.

Google Sheets

Google Sheets is the unsung hero of prototyping data-driven ideas. Contrast the following things to Postgres:

Want to move around some data? It’s as easy as marking some cells and copy-pasting them around.

Want to rename some columns and change their data type? Just rename them, don’t bother about the data type.

Want to enter some data from your phone? No problem, download the Google Sheets app and start typing. Syncing included.

Of course, there are drawbacks, too. No data validation being one of them. You accrue technical debt by having to parse the data later on (everything will be a string) but for this purpose the benefits outweigh the drawbacks.

Data Structure

I decided on a fairly simple data structure. Everything is an event. An event has four fields:

  • datetime (datetime string)
  • category (string)
  • event (string)
  • value (number, can be empty)

The datetime is the time of the event. Cool trick: In Google Sheets (on macOS), you can enter the current datetime by selecting a cell and pressing +Option+Shift+;.

The category would be something like foodactivity, etc.

The event describes what actually happened. For example gym or rice. I’ll get back to that later.

The value is a number (float or integer) in case the event was something measurable. For example, a weight event could have my weight in kg as a value. This value is optional.


Check out this example in which I enter some data:

Types of Data

Brain Fog, Extroversion, Libido

I recorded these three parameters on a subjective scale of 0 to 5. I came up with the choice of these parameters because they were the ones which I noticed the most and also which changed quite a bit.

Brain Fog

Defined by the question: “How foggy / unconcentrated do you feel right now?”.


A measure of how extroverted I’d feel. More specifically, trying to measure my openness of doing things which cost social “energy”, e.g. talking to someone new, going to an event (e.g. a Clojure meetup) or working from a random coffee shop.


Pretty obvious. The minimum of libido is defined as “I don’t care whatsoever” whereas the maximum is pretty much “I care a lot because I can’t think straight”.


Also, I kept track of every time I had a, let’s say, a significant deployment event on the toilet. I even rated it on the Bristol stool scale! As a great side effect, I now know the Bristol stool scale by heart.

The idea here was that maybe I would be able to predict the type of my stool. How cool is that. So useful.

Google Fit

You remember those Sundays when you don’t leave the house and then feel kinda sluggish the whole day? I had this theory that leaving the house and walking around would be beneficial by reducing Brain Fog. Luckily, Google had already been recording my location history of the past two years (thanks!) and the Google Fit data even includes the step count every few minutes or so.

Fetching Google Fit data is surprisingly easy via Google Takeout. You end up with some CSVs. I wrote a parser to import the data.

Emfit QS

Thinking back to those Sundays, I often tend to sleep unreasonably long. I didn’t want to buy a smart watch and tracking sleep accurately without a smartwatch is actually not that easy. Finally, I decided this would be a great excuse to buy the Emfit QS which is a sleep tracker which you put under your mattress (!).

It continuously measures you heart rate, respiratory rate and movements. Of course, it also calculates your sleep duration. And guess what, you can also export the data as CSVs! I wrote another parser for that.


Analyzing time series data is non-trivial; the two weeks of statistics I had in medical school were definitely not sufficient for that (nope, doctors don’t have much education in statistics).

I coincidentally came across this weight-loss project on GitHub via Hacker News. The approach was simple, but really clever: He would write down his weight and what he did the last day in keywords. This would mostly include food types and sleep habits.

Then, he would fit a machine learning model (vowpal-wabbit, basically a type SVM) on this data with the goal of predicting his daily weight changes based on what he did on the prior day.

Finally, he would output the weights of the model, specifically the weight of each of the keywords (food, sleep habits etc.). Then he would end up with a list of factors where he could see which factors are correlated with weight loss vs. which factors are correlated with weight gain. Definitely check out the repository for some cool figures!


My idea was to use the same approach with some minor modifications: Firstly, I would not have a fixed time window like in the weight loss project – there, it was always about 24 hours as only the prior day was taken into account for each weight measurement. Instead, I would be able to define different time windows and see if this changes my results.

Secondly, I wasn’t interested in my weight but in other things. I would run separate analyses of all the parameters I had been recording (brain fog, extroversion, libido, poop score). That means that I would have to run multiple analyses.

Vowpal-Wabbit Results

Turns out, it didn’t work. For Brain Fog, I would get a training accuracy of only 0.3 which is better than guessing (0.16) but really not all that good. The training accuracy of the other parameters (including poop) turned out to be similar.

Simple Correlations: Reviving the Incanter

I decided I had to take a step back. I was just stupidly feeding data into Vowpal-Wabbit in the naive hope that some magical result would come out. In the case of the weight-loss repo, that turned out to be the case. In my case, not so much.

An important aspect of data science is actually getting a feel for your data. This includes plotting some things. Just “plotting some things” in Clojure is however not something which people do very often. People nowadays use Python for Data Science (why?) so most well-maintained, fully-featured packages are in that ecosystem.

There is however a hidden old gem to be found: The Incanter project. In the old ages of Clojure, this was a promising and active project. Sadly it’s hardly maintained nowadays. The good news is that 1) Clojure’s backward compatibility is fantastic and 2) Incanter’s API is easy to understand.

Plotting some things is actually really straightforward. Ironically, it’s probably easier to plot things with Clojure and Incanter than with a Jupyter Notebook and matplotlib. Having a simple, functional API is much more developer-friendly than the concoction of MATLAB-inspired, mutable-state things of matplotlib.

Simple Correlation Results

I couldn’t shake the feeling that my sleep was a central factor in causing my Brain Fog. I therefore correlated values of the Emfit QS for each night with my Brain Fog the next day. There are lots of values which are provided: Total sleep duration, duration in REM sleep, duration awake (but in bed), and count of awakenings just to name a few. While correlating all these values with my Brain Fog, I had to think of this xkcd about multiple testing.

Interestingly, the total sleep duration correlated with my Brain Fog – positively! That means that the longer I sleep each night, the higher my Brain Fog would be the next morning.

This is quite puzzling.

From intuition, longer sleep should actually correlate negatively with Brain Fog, right? The longer you sleep, the more focused you’re going to be the next morning and the less Brain Fog you’re going to have, right? That didn’t seem to be the case.

One of the reasons may be that I didn’t have a broad range of sleeping durations – the range was between 6 and 10 hours. I could imagine the curve to actually be U-shaped – high Brain Fog for very little sleep (say, 0 to 4 hours), low Brain Fog for intermediate sleep durations (4 to 7 hours) and high Brain Fog for long sleep (7+ hours). But I’m just guessing here.

More interstingly, the correlation was even stronger for duration spent awake in bed. That again makes sense: I remember those Sunday mornings when I’d hang around in bed for ages just to get up and feel groggy the whole day.


Even though I didn’t come away with a clear result, I learned a lot of things.

Firstly, it’s not necessarily a good idea just to collect a large amount of data and whip it into a Machine Learning model and hope that interesting results emerge from it. Even though we live in the age of Machine Learning, this still doesn’t spare us from putting thought into what we want to investigate and what type of data we want to collect.

Secondly, doing these sort of medical studies, you should always prefer objective values over subjective ones. This may sound obvious, but it’s not: I was measuring Brain Fog on a subjective scale from 0 to 5 and only later switched to a more objective concentration test (Stroop Test).

Thirdly, doing Data Science in Clojure is hard. It’s a truly great language (and it continues to undoubtedly be my language of choice) but Data Science and Machine Learning are fields which are particularly dependency-heavy. You really don’t want to implement your own plotting library or SVM for a small side project. So while the preprocessing is great in Clojure and arguably faster than in Python (both in development and execution), the analysis is more frustrating and takes more time.

Finally, I really don’t get a lot of fun out of Data Science. Compared to building tangible things like web apps, this feels so unrewarding.

That notwithstanding, it actually piqued my interest for this whole medical self-optimization field. Follow-up experiments are already underway. Stay tuned.


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