Category Archives: self-experimentation
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.
After reading about importance of maintaining body’s pH balance, I embarked on a self-tracking experiment in January, with the objective to see how often my pH balance changes on a daily basis and whether my diet has any effect on it. To do so, I measured my PH every morning and evening using litmus testing strips (I used Phinex diagnostic pH test strips). The strips proved to be a convenient and reliable way to track pH level on a daily basis, but I did not learn much from tracking it.
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.
On his Insomnia blog, famous Sleep Doctor Michael Breus describes simple method that you can use to “hack” your sleep cycles and find the optimal time to go to bed. According to this method, you change your bedtime every 3-4 days in 15-minutes increments, until you can wake up without alarm most of the time. This January, I put together a little self-experiment, with the objective to test this method, and hopefully, find my perfect bedtime. Between January 7th and February 1st, I was going to bed at 11:00, 11:15, 11:30 and 11:45 (5 consecutive nights for each time slot), and waking up at 7 am every morning. At the same time, I was using Zeo, my rTracker log, and a couple of apps to track my sleep and some other data. While results of my experiment were not exactly perfect, I believe it still helped me to identify my optimal bedtime slot.
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.
In January, I started experimenting with two-dimensional approach to measuring mood. This approach was mentioned in one of the posts on Quantified Self website, but basically, in addition to using the “valence” scale (I feel bad/good), you rate your mood also on dimension of “arousal” (how “hyped” you feel). This weekend, with over 30 days of data, I had a chance to look at how well does this two-dimensional mood metric reflects my state of psyche, and ended up with an awesome visual map of my emotional states which confirmed that I can potentially drop the individual emotions from my self-tracking log.
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.