january quantified self projectsIf you have not noticed yet, the “modus operandi” for this blog and my self-tracking efforts is a bit different this year. The month of January was spent collecting data, testing apps and services, and blogging about various QS issues, and month of February was dedicated to analyzing collected data, reviewing tools, and sharing insights and recommendations. So I thought it would be helpful if I summarized briefly what I learned during the last two month, in one post.


I conducted a month-long experiment in order to find the optimal “lights off” time. During four weeks in January, I would go to bed at specific time for a week (11:00 pm, 11:15 pm, 11:30 pm, and 11:45 pm), while tracking different properties of my sleep using Zeo Mobile and rTracker log . At the end, I compared sleep metrics, as well as physical and cognitive performance data on the following data across four conditions, and concluded that the best time for me to go to bed is between 11:00 pm and 11:15 pm. You can read more about this experiment here.

I also tested Sleep Cycle app along with Zeo and BodyMedia, in order to compare their sleep metrics afterwards. I did not have a chance to analyze that data yet, but I will put the entire January dataset soon, so if you are interested, you can run correlations yourself.

Finally, I started tracking dream recall and content of my dreams: I believe that with enough data, I may be able to find connection between the content of my dreams and previous (or even upcoming!) day experiences and psychological conditions. If you think I am crazy, check out some actual scientific studies that were done in this area.  Here is how my dreams looked in January:

Analyzing Dreams Content

Measured Me: Tracking Content of Dreams


In January, I tested three new tools: 80 Bites app to track my eating habits, pH testing strips to see how my pH balance changes from day to day, and SweetBeat app to test sensitivity to food. I learned that I eat my breakfasts too fast, and the time spent chewing is related to tastiness and weight of the meal. I also learned that SweetBeat is a great tool to test if you are sensitive to certain ingredients in food (in my case, it was smoked turkey). I also learned that my salivary pH levels are stable and within the healthy ranges, and that NutritionData’s fullness factor is a useful food metric for planning meals.

Fitness and Health

Analyzing data from my BodyMedia tracker allowed me to prove that three different fitness classes at my gym are equally efficient at burning calories. I also discovered that good old “orthostatic” test (comparing your standing and resting heat rate) taken in the morning can predict how I will be feeling and performing for the rest of the day.


In an attempt to derive my personal formula of happiness, I tested three psychological and behavioral theories. Specifically, I looked at how happiness can be predicted by tracking life satisfaction, differences between my expectations and actual fulfillment of my life priorities, and time spent on various everyday activities.  So far I learned that resilience, autonomy, creativity, health and time with loved ones positively affect my happiness. The results of this analysis were presented at the New York Quantified Self meetup earlier this month, and here is the video of my presentation.

I also experimented with alternative approaches to measuring mood and emotional states, and in January, tested a 2-dimensional measurement of mood. The two dimensions, pleasure and arousal, turned out to be a great substitute measure of emotions. It looks like I may end up dropping individual emotions from my log, because two mood scales are sufficient to capture my relative emotional state any time of the day.  The new metric of mood was only somewhat predictive of stress and happiness, which is ok.


After several weeks of testing, I had to revise my measure of creativity. The new version is being tested this month, so I will keep you posted!


There was not much progress here. I am still working on developing a “financial fitness” score, based on Mint and Ace Budget app data.

Phew!! It looks like I learned a lot in the past two month. Of course, March is here, and it’s time for new projects. Measured Me experiment continues, so stay tuned!

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