Tag Archives: quantified self
One of the important factors that influence your happiness and quality of life, in my opinion, are your personal values and whether you live according to your value priorities. Modern psychologists define values as “guiding principles of your life”, and, depending on the method, values can be measured in terms of absolute or relevant importance. In September and
October August, I tracked my personal values in order to assess their stability and to see how they interact with other variables in my everyday life. In this and the following post, I will share some interesting insights I was able to get from that data.
If you live in New York, you probably already know that NY Quantified Self community has held its quarterly meeting this Thursday, November 15. I have been attending NYQS meetups regularly for quite some time now, but this time I was especially thrilled because I helped to organize it. With overwhelming turnaround, engaging Show&Tell sessions, creative presentations, and community interaction and networking afterwards, the meetup, as always, exceeded my expectations. In this post, I will sum up the presentations that made this event so informative, inspirational and thought-provoking.
I was rereading my last post, and realized that I omitted one more potential way to monetize self-tracking apps: distributing them among health and fitness practitioners. The idea of prescribed apps has been entertained by medical professionals for a while now. Think doctors that prescribe an app along with the pills; patient then uses the app to track symptoms before and during the treatment. Think personal fitness trainers that offer the app to their clients so they could track their progress in losing weight or gaining muscles. The app in this case is a tool that will provide an objective unbiased feedback to both sides. What do you think?
In my previous post, I started describing the quantified self app that I would have built myself, if I had access to necessary skills or resources. Originally, these notes were intended for software developers and enterpreneurs who contacted me after reading my post about my search for a good tracking app. After putting the notes together, I thought that it would be only fair if I shared them publically, so other developers, and Quantified Self practitioners like myself, could have a word in further discussion, should it ever ensue. This last portion of the notes will focus on analytical features of the app, data portability, and potential methods for monetization.
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.
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!
A couple of months ago bioanalytics company Inside Tracker tweeted a nice one-time discount on their services, so I jumped at this opportunity and purchased their DIY plan. The cheapest of all (after discount, I paid $34.30 instead of regular $49), DIY plan allows you to upload your blood test results to receive personalized nutrition, lifestyle and exercise recommendations. I finally got a chance to test drive their services, and will share my personal experiences in this post.
Check out this awesome interactive map of the quantified-self tools, created by Rachelle DiGregorio (please head to the site to play with the original map).
Whether you are conducting a self-experiment, or tracking some variables in your life simply out of curiosity, eventually you would want to look at the data and examine it for some meaningful patterns. One of the most common research questions is testing differences: you would like to see if a given variable differs with respect to a certain “grouping” aspect, and whether these differences are statistically significant. For instance, you may want to see if you sleep better on the nights after gym workout, or if a certain diet helps you to lose more weight. In this post, I will provide simple step-by-step instructions for conducting difference test that can be easily done in Excel. I promise to keep demonstration basic and as less technical as possible!