Category Archives: health
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.
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.
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”.
As I mentioned in my previous post, starting this month my diet tracking efforts are focusing more on nutrient qualities of food, eating habits, and effects of diet on body and mind. One of such effects is food sensitivity, a non-allergic instances when your body negatively reacts to specific foods or ingredients (don’t confuse with food intolerance). Such negative reactions may manifest themselves in physiological or psychological symptoms, including fatigue, bloating and gas, nervousness, changes in mood, acid reflux, and migraines. Often, we attribute these symptoms to our busy lifestyles, stress, and health conditions, not knowing that they could be easily eliminated by simply changing our diet. In this post, I will share my experiences with SweetBeat app and its awesome food sensitivity detection feature. To my knowledge, it is the first app that lets you test your body’s reaction to meals and isolate potentially harmful foods.
Have you ever had those days when you have been feeling fine and then all of sudden you feel crappy? Or, on the opposite, you wake up in a bad mood, but as the day progresses, you are getting better? If you track your mood (or any other psychological state, to that matter) several times a day, there is a way to quantify severity of these “swings”. In this post, I will show how to calculate the “mood swing scores”, and how you can use these scores to learn more about changes in your mood and what causes them.
In this post, I would like to raise a question that in some degree reflects my personal concerns about current trends in Quantified Self movement. Since this opinion is based primarily on my personal observations, I would really appreciate any feedback or comments from QS community. Please correct me if I am wrong, but why do we have so many tools and projects that focus on diet, sleep, exercise, but when it comes to tracking psyche, in particular, psychological states and traits, the inventory and range of QS projects is rather limited?
Last month I looked at my sleep data produced by Bodymedia and Sleep Time app, statistically comparing their sleep quality scores with each other, and with my own subjective sleep assessment. In October and first weeks of November, I replicated experiment, adding another device – Zeo. With over 30 nights of data, I finally was able to look at four different metrics, side by side, to see how comparable and interchangeable they are. The results will surprise you.
If you downloaded my September data (you can do it here, absolutely free!), you probably noticed that the research agenda behind data collection that month was focusing primarily on diet, exercising (#fitsperiment!), and sleep. I finally found time to look closer at some of that data, and in this post, will share some interesting results of my sleep data analysis.
It is a well known fact that weather can influence various aspects of our everyday lives, including physical and mental health, productivity, performance, social behavior, etc. Sometimes, the connection is direct and obvious. For instance, extreme temperature fluctuations have been shown to affect our immune systems, and the quality of air is directly linked to asthma and allergies. More than often, however, the weather effects are peculiar and more subtle. The heat, for example, has been linked to aggression and violence. Certain kinds of wind has been shown to negatively affect human behavior and psyche (e.g., the foehn in Swiss Alps, or khamsin in Middle East). A lot of people (including myself) report sleeping better at nights when it rains or snows. I personally tend to experience mild depression on cloudy and rainy days, while plenty of sunshine usually affects my mood positively. Naturally, the only way to see if any particular weather aspect actually affects your life, and to what extent would be to include it in your self-tracking routine, and then analyze the hypothesized patterns. So for the past couple of weeks I have been looking for a way to incorporate weather data into my tracking logs, and in today’s post, would like to share my current findings and potential quantified-self research ideas.