Category Archives: self-experimentation
I have noticed that a lot of Quantified Self folks and biohackers are interested in longevity research. Personally, I never could understand their fascination with extended life. While I am not completely AGAINST the studies in this area, I regard the whole concept more cautiously and with much less enthusiasm. Lately I have been jotting down some thoughts on this topic, and would like to share them in today’s post. Please note that these are just half-baked thoughts, and I am yet to write a full post (hopefully, sometime in the future).
As promised, posting today the slides of my “100 Days of Summer” talk that I presented at the 2013 Quantified Self conference in San Francisco last Thursday (October 10). In this presentation, I shared most important and interesting results of the first phase of Measured Me project.
Talking 20 is a young biotechnology start-up in California that aims at making low-cost, at home blood tests that could be used to track twenty essential amino acids (hence the name). In October 2012, I responded to their call for support on Twitter and purchased “T20 Starter Pack” home kit for ten dollars. I paid 12 dollars (2 dollars to cover shipping), and a couple of weeks later received the kit, which I mailed back to them in December. After waiting patiently for ten months, I can finally share with you what I learned from my blood test. Drumroll, please…
In this post, I will discuss three invisible temporal patterns that are likely to be present in your self-tracking data and which, if ignored during analysis, may lead to erroneous conclusions and interpretations. I am talking about trends, social rhythms and intra-day variability.
The ultimate purpose of self-tracking, in my opinion, is control. Control over health conditions, performance, mood, and other aspects of your everyday life. To paraphrase the famous adage, we measure so we could manage. We also measure things that we can’t manage (e.g., weather). Because self-discovery through self-tracking leads to knowledge, and knowledge is another form of control. Or at least, it gives us a sense of control. But how much of our everyday life we can actually control? How this lack of control affects our everyday life? To some of us, these questions may sound philosophical, but I believe the answers can be found in our own data. In February and March, I have been tracking “entropy” in my everyday life: those occasions when things go beyond my control, and happen exclusively due to some external forces. The ultimate objective was to investigate to what extent such random uncontrollable events influence different aspects of my life.
I have been using RunKeeper to keep track of my walks and bike rides for a while now. In addition to distance and pace, RunKeeper offers an estimate of calories burned that is most likely derived based on my weight/age and distance information. Last month I had an idea to compare these estimates with those provided by my Bodymedia tracker, and to do that, I had to conduct an experiment, which lasted for about two weeks. The estimates provided by both trackers turned out to be very close.
Self-esteem refers to individual’s emotional evaluation of his/her own worth and personal abilities and capacities. Psychologists believe that self-esteem influences how we feel, act and relate to other people, which makes it one of the central concepts in positive psychology. In general, self-esteem considered to be a trait (a psychological construct that is relatively stable over-time, like personality characteristics), although some psychologists recognize also more short-terms expressions of self-esteem (self-esteem states). I have been tracking my self-esteem since February, and this week finally had a chance to look at the data. I was particularly interested to see its stability over time and throughout the day, and how self-esteem is related to everyday stress, mood and happiness.
Can you predict your day based on how you feel immediately upon waking up? In an attempt to replicate Sami Inken’s analysis, I have been rating my mood every morning within 15 minutes of waking up, and then three times throughout the day: in the morning (around 11 a.m.), afternoon (around 5 p.m.), and evening (around 10 p.m.). After about a month of tracking (22 days of data), I looked at the correlations, and the results were rather disappointing.
The March is almost over, so I thought it is a good time to tell what kind of things I am have been tracking and what self-experiments I have been conducting this month. As usual, at the end of the month I will export data from my rTracker log , analyze it and will share any interesting insights and findings with you.
If 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.