*My Quantified Self Analysis: Sleep Time and Last.fm

If every online click is being registered in a global dataset also known as Big Data, our day to day habits can also be tracked. With the advent of digital technologies, human themselves become data; or at least, through the emergence of the Quantified Self movement and its tracking tools our behaviours, practices and daily routines can be projected into raw datasets, and then, closely scrutinised. This following experiment firstly focused on acquiring data from a mobile application called Sleep Time and developed by Azumio; secondly, online streaming platform Last.fm will also be referred to as device that tracks down and records the user’s music streaming activity.

1. Sleep Time

Three days are going to be utilised in order to analyse my own sleep cycle: October 21st, 22nd and 23rd. Through the following graphs, the average bed time and the average duration will be analysed by comparing the aforementioned selected days.

Afterwards, each day will be analysed individually. On October 21st, the set bed time was 23:47 and the waking time 07:23, so the entire sleeping activity lasted for 7h and 36 min. Out of this, I was awake for 44 minutes; Light Sleep counted for 3h and 16min and Deep Sleep for 3h and 36min drawing to a conclusive efficiency of 6h and 52 minutes.

On October 22nd, the initial time of going to bed was 23:07 and the rising up time 8:56 so the entire activity tracked by the app lasted for about 9h and 49min. Out of this period of time, I was awake for 0:21 minutes; Light Sleep counted for 4h and 54min and Deep Sleep for 4h and 43min drawing to an eventual efficiency of 9h and 28 minutes of actual sleeping.

On October 23rd, the set bed time was 22:53 and the waking time 8:39, so the entire sleeping activity tracked by the app lasted for about 9h and 46min. Out of this tracked period of time, I was awake for 20 minutes; Light Sleep counted for 5h and 1min and Deep Sleep for 4h and 24min which draw to a conclusive efficiency of 9h and 25 minutes.

2. Last.fm

Through Last.fm I tracked my most played songs that have been played on every personal device where the Last.fm application is installed on (my laptop and my phone). Under the conditions that I haven’t used the service that often lately, I will analyse a broader period of time: the last 12 months of activity.

As the above graphs show, my most played bands are The Drums, The Hives and The Horrors. Both graphs contain an interesting trend regarding the band called The Drums. Under these circumstances, I will focus the rest of this analysis on this particular band.

Further on, if I click on the 340 plays The Drums has registered in the past year, I can see their most popular albums and most popular songs with their respective amount of plays for each track/record.

By clicking on the most popular song (What We Had with 37 plays), I can see the exact time and date when the song was played in the past year. As the application requires continuous Internet connection, the cluster of songs generated on the 4th of February is caused due to the fact that I listened to the song when I did not have an active Internet connection; so, when I went online for the first time, all the plays registered with the same date and time (4 February 2014, 15:58).

It is interesting to look into the overall listening trends of the band itself. With over 17million plays and nearly 800k listeners, I contributed to The Drums’ official library with a total of 683 plays. Here, this projection tracked the trend of the band by taking into account the number of plays registered each month, starting with May 2014 and finishing with October 2014 (the low-high interval is roughly situated at 9k - 15k).

As a platform that gives users the possibility to socialise and interact with other users, it is an engaging exercise to look into my relationship with other ‘user friends’ by taking into account The Drums as the connection point between users.