After abandoning my Fitbit device in January because using it didn’t see improvement in my weight (see previous post), I was wondering if I could still measure my risk to develop cardiovascular diseases and other preventable chronic diseases (diabetes e.g.). So, still sitting at my desk (something I do for more than 8 hours a day in theory - probably more in practice), I looked into the ways to monitor my risk for these diseases …
Statbel, the Belgian governmental organisation for data and statistics, just released mortality data for 2014 ( press release in French, dataset). The headline of their press release was that, for the first time, tumors were the first cause of death for Belgian men. Diseases of the circulatory system remains the main cause of death in Belgium, for women and for both sex together. While the death of someone is a bad news in itself, I’m more interested here in the evolution of death causes. I’m interested in the evolution of causes of death because it might be a consequence of the evolution of the Belgian society and, as a proxy, of any (most) developed, occidental countries. ...
With a few days of interval I received two very different ways of reviewing data collected by users of “activity trackers”. The first one came from Jawbone (although I don’t own the UP, I might have subscribed to one of their mailing-lists earlier) and is also publicly available here. Named “2013, the big sleep” it a kind of infographics of how public (and mostly American) events influenced sleep of the “UP Community”. Here data about all (or at least a lot of) UP users were aggregated and shown. This is Big Data! This is a wonderful and quantitative insight on the impact of public event on sleep! But this is also a public display of (aggregated) individual data (something that UP users most probably agreed by default when accepting the policy, sometimes when they first used their device). ...
After a bit less than 2 hours, jepsfitbitapp retrieved my sleep data from Fitbit for the whole 2013 ( read previous post for the why (*)). Since this dataset covers the period I didn’t have a tracking device and, more broadly, I always slept at least a little bit at night, I removed all data point where it indicates I didn’t sleep. So I slept 5 hours and 37 minutes on average in 2013 with one very short night of 92 minutes and one very nice night of 12 hours and 44 minutes. Fitbits devices do not detect when you go to sleep and when you wake up: you have to tell tem (for instance by tapping 5 times on the Flex) that you go to sleep or you wake up (by the way this is a very clever way to use the Flex that has no button). Once told you are in bed the Flex manages to determine the number of minutes to fall asleep, after wakeup, asleep, awake, … The duration mentioned here is the real duration the Fitbit device considers I sleep (variable minutesAsleep). ...
After previous posts playing with Fitbit API ( part 1, part 2) I stumbled upon something a bit harder for sleep … Previous data belong to the “activities” category. In this category it is easy to get data about a specific activity over several days in one request. All parameters related to sleep are not in the same category and I couldn’t find a way to get all the sleep durations (for instance) in one query (*). So I updated the code to requests all sleep parameters for each and every day of 2013 … and I hit the limit of 150 requests per hours. ...
I continue to explore data about my physical activity in 2013 ( see part 1). We moved from an apartment (on the third floor of a building) to a house (with two floors) on July 1st, 2013. I was wondering if the change would have an impact on the number of floors I climbed: I now have to climb to reach bedrooms and go down to go in the living room. A standard house. ...
2013 is near its end and it’s time to see what happened during the last 360 days or so. Many things happened (graduated from MBA, new house, holidays, ill a few days, …) but I wanted to know if one could quantify these changes and how these changes would impact my daily physical activity. For that purpose I bought a Fitbit One in March 2013. I chose Fitbit over other devices available because of the price (99 USD at the time) and because it was available in Europe (via a Dutch vendor). At that time the Jawbone Up was unavailable (even in the USA) and the Nike Fuelband couldn’t track my sleep. ...
The Open Knowledge Foundation (OKF) released the Open Data Index, along with details on how their methodology. The index contains 70 countries, with UK having the best score and Cyprus the worst score. In fact the first places are trusted by the UK, the USA and the Northern European countries (Denmark, Norway, Finland, Sweden). And Belgium? Well, Belgium did not score very well: 265 / 1,000. The figure below shows its aggregated score (with green: yes, red: no, blue: unsure). ...
A few months ago Google decided to retire its Google Reader (it stopped working on July 1st, 2013). As it was simple, effective and good-looking, a lot of people complained about this demise. A few days ago The Old Reader, one of the most successful replacement for Google Reader, also announced it will close its gates, only to keep early registered users. And today Feedly, another successful alternative, announced it is introducing a pro version at 5.00 USD per month. ...
A few days ago Prince William and Duchess Catherine of Cambridge gave birth to Prince George. Today at the office we were wondering if we will see more babies names George in UK. Very important question indeed! So I went to the UK National Statistics website and looked for baby names in UK. Let’s focus on England and Wales only. There are two datasets for what we are looking for: one for the period 1904-1994 (by 10 years steps) and one for 2004 (if we want to be consistent with the 10 years step in the first dataset). I extracted the ranking relevant for us here: for babies called William, George (and Harry, William’s brother). The data is here. ...