Category Archives: popular
Ok, some of you may find this post a bit unorthodox. I was always curious to see if the moon has any effect on my behavior and psychological states. While a traditional researcher inside me still has doubts about this kind of “analytics”, I still went ahead and crunched some numbers, just for fun. Using my September and October data, I analyzed the cyclical patterns and differences in my sleep quality, mood, and stress across four moon phases (new moon, first quarter, full moon, last quarter). Interestingly, there were some notable differences in mood and sleep during full moon and new moon phases.
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.
After becoming fascinated by the Quantified Self movement a couple of years ago, I have immersed myself in self-tracking, and have been using this blog to document my personal experiences and thoughts, and review different tools and measures that I tried. When I started this blog, the scope of topics was limited to physical and mental health and fitness, but this list has been growing since then. The objective of the Measured Me project has evolved and become more ambitious than ever. I am now searching for the best ways to measure and track as many aspects of my everyday life as possible. But what is it exactly that I am trying to achieve? What is the ultimate goal of this ongoing experiment? In this post, I will discuss the new raison d’être behind the Measured Me experiment – theory of life optimization.
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.
After posting about my search for the best mobile app for self-tracking, I received several e-mails and DMs from developers, asking me to advise more on specifics of the “perfect” app. In response, I offered to write “notes for developers” from the end user perspective, describing the app just as I, Quantified-Self practitioner, imagine it. In this, and the following posts I will share these notes, along with more elaborate details, concrete examples, and even some monetization ideas. In other words, I will describe the app that I would have built myself, if I had necessary resources or skills. I hope that this post will be interesting not only to developers, but also to other folks involved in Quantified-Self movement.
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.