Category Archives: personal analytics
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.
One of the diet-related tools that I tested in January was 80 Bites app. Dubbed as a “pedometer for your mouth”, this simple app let’s you track how many “mouthfuls” (“bites”) of food you take during the day, and how much time, on average, you spend chewing the food between the bites. The premise behind the app is that eighty “bites” a day is usually enough to feel full and satisfied, and limiting your food intake on the long run can help you to shrink your stomach and eat less. I am not sure about the latter, but using this app for several weeks definitely helped me learn to eat my meals more mindfully. I also discovered something new and interesting about my eating habits from the data that I collected.
As promised, posting the full Power Point slides of my “Hacking Happiness” presentation from NY Quantified Self meetup. In this self-tracking experiment, I looked at how different aspects of well-being, personal values and everyday activities predict my happiness.
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.
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.
I am very excited to start this year with some new awesome self-tracking projects and experiments! For now, the major emphasis remains on areas like sleep, fitness, diet, cognition, and psyche, but I will be covering a bit productivity and finance, too. Here is a quick preview of what I am tracking (and working on) this month.