Category Archives: personal data
In December and January, I have been tracking some aspects of my everyday life in order to test a couple of psychological and behavioral theories of happiness. The preliminary results of this experiment will be presented tomorrow at the Quantified Self NYC meetup, and of course, I will be posting the PowerPoint slides of my presentation later this week. Unfortunately, 10 minutes are not enough to cover everything in depth, so I thought I would dedicate a separate post that would discuss the most interesting findings of this experiment in more detail. My personal favorite was quantification of how not being able to live according to my personal values affects my happiness.
I continue crunching my January data, and in today’s post, will discuss a simple heart rate test that I have been performing last month every morning in order to evaluate its predictive power. It’s called orthostatic test, and it is widely used by athletes to assess their physical condition after the training. All you need is a stopwatch or a heart rate measuring app (I personally used Azumio’s Instant Heart Rate app). Here is how you perform the test. The moment you wake up, try to stay still in bed and take your pulse measurement. That would be your resting heart rate (RHR). Then get out of bed and after standing for approximately 15-30 seconds without making any sudden movements, measure your standing heart rate (SHR). Now calculate the orthostatic heart rate (OHR): OHR = SHR – RHR. If you track your OHR for several weeks, you will notice that most of the times its values stay around the same, but occasionally will go higher. Those “spikes” in OHR happen the night after you overtrain at the gym, or if you don’t get a good night rest, get sick or because of some other disturbance in your autonomic nervous system. My theory was that since OHR measures the “recovery” of the body, perhaps, by looking at OHR in the morning, I would be able to predict how I will feel later that day. My self-tracking data partially confirmed that hypothesis: the morning OHR numbers can predict physical and mental performance later in the day.
In last post, I showed how I mapped 24 emotional states against two dimensions of mood (valence, a.k.a. pleasure, and arousal) using my self-tracking data from January. The resulting “map of emotions” proved that tracking mood using two-dimensional approach is more effective than using a single question (e.g., “how do you feel”). It also showed that I can drop the individual emotions from my log and use only mood dimensions to capture my emotional states. In this post, I will share results of additional analyses. Specifically, I looked if pleasure and arousal dimensions of mood can replace stress and happiness measures.
This year I decided to “outsource” my gym workout, and instead of experimenting with various workout routines from fitness magazines and blogs, joined my gym’s “Total Body Conditioning” classes. The classes are completely free and are held every Monday, Wednesday, and Friday between 12:30 p.m. and 1:15 pm. Each class is led by a different trainer, and as a result, the routines and pace of the workout considerably vary across the days. For example, on Wednesdays, the focus is more on weights and core training, whereas on Mondays and Fridays it is mostly plyometrics and cardio. Curious to see if classes differ in terms of efficiency, I turned to Bodymedia and my own self-tracking data. Which class burns more calories and brings me closer to my six pack abs? The answer was just a couple of calculations away.
When you, like me, are on a 6-7 meals a day diet plan, the main challenge is to make sure that you are not getting hungry too soon between the meals, but feel hungry enough when the time comes for your next meal. That means carefully controlling the portions and ingredients that go into your food. Could quantification and self-tracking help in this case? This month, I started experimenting with “satiety” of food and ways to measure and predict it. In this post, I will discuss the properties of foods that are predictive of satiety and hungriness, including one particularly interesting diet metric: “fullness factor”.
It happens to all of us: one day you forget to charge your Zeo or Bodymedia, go out of town on a business trip, come down with nasty flu, or simply forget to fill out a section in your self-questionnaire. Your self-tracking routine somehow gets interrupted, and some questions are left unanswered. As a result, your self-tracking data now has some gaps and holes. In this post, I will discuss why you should not be really concerned about it, and how to correctly “patch up” your data, if missing values still bother you.
When I made my September self-tracking data available for download, I thought I should include some sort of disclaimer. Not to limit access to data, but rather protect myself from potential liabilities associated with use, analysis and interpretation of my data by other parties. I turned to the Internet to find out how this disclaimer should look like, but to no avail: most of the discussions around Quantified-Self data so far have focused on data portability and privacy issues. I then turned to Twitter, Facebook, and Quantified-Self forum. My post on Quantified Self forum actually resulted in small but somewhat heated discussion, so I thought I should elaborate more on this issue in a separate post. So today I would like share my thoughts on some potential legal pitfalls associated with publishing your quantified-self research and data, and how they could be avoided by including a disclaimer.
A couple of weeks ago a small wellness startup from UK contacted me with request to share some of my self-tracking data. They are working on a presentation for potential investors, and are planning to include insights from my data as an example of how self-tracking could be used in public health programs. I was more than happy to oblige, not because of the vanity, but because it serves a great cause. It is for the same reason I also just approved unlimited access to my OpenPaths data to a couple of public research projects. And I am not going to stop there. After thorough considerations, I decided that from now on, I will be making some of my self-tracking data available for download, along with the detailed description of the variables that I tracked. My September data is now public and up for grabs, along with the data dictionary (both packed into a Zip file).
Yesterday, following the lead left by Dave Shelton from Wellnowbe in the comment to one of my posts, I went to check out Statwing, a website that lets you perform basic statistical analysis on any kind of data (including data from your QS projects) “on the fly”, right in the browser. I was very skeptical at first, but I have been enjoying Dave’s thought-provoking blog posts for awhile now, and trust his opinion, so I decided to check out this site. I was not disappointed. In fact, the moment I started playing with the tool, I was hooked!
Two months ago, tired of juggling between paper questionnaires and Google Spreadsheets, I embarked on a quest to find the perfect mobile app for self-tracking. The objective was to identify the single app that would enable me to log any kind of personal structured data, in any domain of my life. By structured data I mean data that can be stored as a number or a short text (letter, word or two), as opposed to images, video, sounds, maps, and long texts; think heart rate, weight, responses to psychological questionnaires, etc. In my previous post, I described the search methodology, and how I reduced the initial pool of 185 tracking apps down to 11, applying the versatility criteria. Today, I will narrow down the results even further, and reveal the winning apps.