Category Archives: how-to
The following post was written by Dr. Alan Dabney. Professor Alan Dabney received his Ph.D. in biostatistics from the University of Washington in 2006. He joined the faculty in the statistics department at Texas A&M University later that year and received tenure in 2011. Dr. Dabney conducts research in the analysis of “big data,” particularly the kind that originate from biological applications; for a list of his research publications, please see his Google Scholar profile. In addition to his research activities, Dr. Dabney is an award-winning teacher of both undergraduate and graduate students in the statistics department at Texas A&M. He is also active in the creation of non-standard educational media that is broadly accessible. Examples include his upcoming “graphic novel,” The Cartoon Introduction to Statistics, and his featured role in W.H. Freeman’s Stat-Clips video lecture series. He can be reached at adabney (at) stat.tamu (dot) edu.
Winslow Strong of Biohack Yourself (check out his awesome blog, by the way!) recently posted a great question on Quantified Self Facebook page, asking about ways to quantify restraint. This is when I remembered about my attempt to track willpower in March that was partially inspired by our conversation with Hiren Patel of Becoming the Best (another awesome blog!). My method for tracking willpower (self-restraint/self-control) was rather simple and straightforward.
The pursuit of creativity and self-expression are among the personal values that influence my happiness. Unfortunately, most of the creativity tests that exist today require you to perform certain tasks (e.g., solve a problem, draw something, etc.), involve other people rating your performance, and thus are not suitable for everyday self-tracking. I needed something more simple and more general, so one of my Quantified Self challenges this year was to develop a method to measure and track my creativity on a regular basis. After several unsuccessful tests in January-February, I finally ended up with a 4-question measure that may have a great potential.
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.
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.
A couple of weeks ago, I shared some lessons that I learned while tracking my diet for over six months. One of the conclusions was that tracking diet is one of the most cumbersome aspects of self-quantification/self-experimentation, mainly due to the lack of passive measurement tools and often overwhelming amount of nutritional information to collect and deal with. That naturally let me to the question: how can we make the diet tracking process easier? What would the perfect tool for tracking diet look like? The longer I am pondering this problem, the more I realize that we are talking not about a single stand-alone gadget or app, but rather a small ecosystem that includes digital scale, mobile phone and content. Let me now elaborate.
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.
I must admit that of all my self-tracking efforts that I took on in the past six months, tracking diet turned out to be THE most cumbersome so far. Of course, I am not giving up, and will continue testing and experimenting, until I find the most efficient tools and methods for quantifying diet and its effects on my everyday life. In the meantime, I would like to share some observations and lessons that I learned during the past six months, including what worked and did not work for me, and some interesting insights about my diet and eating habits.
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.