Tag: QS

Time commuting in Belgium

DISO1 – Data I Sit On, episode 1. This post is the first of a series of a few exploring data I collected in the past and that I found interesting to look at again … (I already posted about data I collected, see the Quantified Self tag on this blog)

Life is short and full of different experiences. One of the experiences I don’t specifically enjoy but is integral part of life is commuting. Although I tried to minimize commuting (mainly by choosing home close to the office) and benefit(ed) from good work conditions (flexible working hours, home working, etc.), a big change occurred when I took a new opportunity, in 2015, to work in the Belgian capital, Brussels.

Debian-lover-car-jepoirrier-on-flickr — Traffic jam in Brussels – one of my pictures on Flickr (CC-by-sa)

From where I lived at that time, using public transportation was not a viable option, unfortunately: it implied roughly 2 hours to go one way and changing at least 2 times between bus, train and metro. Anyway Belgium is know for having lots of cars and I benefited from a company car. Since some time, I’m also interested in Quantitative Self so I started collecting data about my daily commute.

What I try to see is the seasonality of commuting (I would initially expect shorter commute time during school breaks), the differences between leaving for work after driving children to school or without driving them, … There is also an extensive literature on the impact of commuting on the quality of life …

So, how did I do that?

The route usually taken, between my home then (in Wavre) and my office then (in Brussels, both in Belgium), is 28km long and the fastest I ever saw on Google maps to drive this distance is about 20-25 minutes.

I took note of the following elements in whatever default note-taking app is there in my phone at that moment (Keep on Android, Notes on iOS). The first field in each row is the date in a %y%m%d format, i.e. year, month and day of month as zero-padded decimal numbers, 2 digits only for each. The second field is the start time in a %H%M format, i.e. hours (24-hour clock) and minutes also as zero-padded decimal numbers, 2 digits only for each. Start time is defined when I enter my car at home, in the morning. The third field is the arrival time (same format as start time), defined as when I stop the engine at work. The fourth and fifth fields are start and arrival times when I go back home, defined and formatted the same way, mutatis mutandis. Any missed start/arrival times is marked as “na” or “NA”. It corresponds, for instance, at times when I leave the office but I stop to meet a client (or more prosaically, to do grocery shopping) before coming back home. I may have missed one or two whole days at max. The data is on Github.

On a daily basis, the little game is to try to figure out which lane is the fastest, if there is a pattern in the journey that makes it faster (I think there is). However, there are so many little things to track in this game that I did not track these small differences. The journey is assumed to take more or less the same route.

At the end, the complete log is saved on my computer and analysed in R (version 3.3.2). The typical measures I’m interested in are departure/arrival times over time, commute duration over time, commute duration per month or per day of the week or per season, … for both the morning and afternoon journeys if applicable. Some funny measures should be the earliest I left for work, the latest I arrived at work, the earliest I left work, the latest I left work, the shortest journey ever (to compare to Google estimate) and the longest journey ever …

An unintended measure here is the amount of time actually spent in the office (on a side note, this is different than productivity – but I didn’t find any unambiguous or flawless measure of productivity so far …). Some interesting variations could be to see the average and median duration of my work days, the shortest day or longest day I had, … (I don’t know if my former employer would be happy or angry to see these results 😉 but note this doesn’t take into account the numerous times I worked from home, even in evenings after having worked the whole day in the office …).

In theory, the fastest I could go is at an average 84km/h (28km in 20 minutes, according to Google Maps, so this is according to traffic, not maximum speed limits). In practice, this is a whole different story …

In a bit more than a year of collected data:

the earliest I left home was 6.11 and the latest 10.11;
consequently, the earlier I arrived at work was 6.32 and the latest 10.36;
the shortest trip to work was 18 minutes and the longest one was 160 minutes (it was on March, 22, 2016, the day of Brussels airport bombing because the office is close to the airport – I still remember);
the earliest I left work was 12.34 (I assume half-day of holidays) and the latest 21.24 (I assume lots of work then);
consequently, the earlier I arrived back home was 12.59 and the latest 21.43;
the shortest trip back home was 7 minutes (there should be some input error here!!!) and the longest trip was 128 minutes (nothing surprising, here, with Brussels traffic jams).

Finally, the shortest stay in office was 242 minutes (4 hours and 2 minutes) – it was that half-day of holidays. And the longest stay in office was 754 minutes (12 hours and 34 minutes).

As always, these things are nice when rendered as graphs …

180215-BxlAllTrafficPoints

A first note it that none of these graphs show any seasonality in the data. At first, I thought I would go faster during school holidays – but it was more a feeling than anything else, as the data show. And although the time at work varied widely over time, the average time spent at work seems to be pretty constant over the year, I was surprised by this:

180215-BxlTimeSpentAtWork

Finally, the time spent in car depending on the departure time is interesting:

180215-BxlTimeSpentInCar

Going to work was clearly split into 2 periods: leaving home (“Start Time”) before 8.30 and after 8.30. That’s because either I went early (and avoided the morning rush hour) or I drove the kids to school and drove to work at the end of rush hour. But although I tried to minimize the journey, the journey after driving the kids to school was still taking more time.

For the evening, going home became a shorter trip if I was able to delay it. And the later I come back, the shorter the trip. (However, if I didn’t drive the kids to school in the morning, the deal is that I would pick them up in the afternoon – fortunately, afterschool care is cheap in Belgium).

All this to come to the quality of life … I didn’t measure anything related to quality of life. I just remember that the first few weeks were very tiring. However, this commuting factor should be added to other tiring factors: learning a new job, adjusting to a new environment, etc. But there is a body of scientific work looking at the quality of life of commuting (I really like this paper as a starter [1], probably because it was published during that period): fatigue, stress, reduced sleep time, heart disease, absenteeism, BMI (weight), … are all linked – in a way – to commuting (either driving or just sitting in public transport).

[1] Künn‐Nelen, A. (2016) Does Commuting Affect Health? Health Econ., 25: 984–1004. doi: 10.1002/hec.3199

And a last point: privacy. This data is from 2015-2016. People who know me (even former colleagues!) know where I worked. And even without knowing me, you know when I leave home, when I leave the office, my pattern of organization, etc. Do I want that? Part of the answer is that I only post this data now, 2-3 years later. On the other hand, here is another free, small dataset!

Next steps? I’m continuing to track my journeys to work, even now we moved to the USA. For privacy reasons, I will not publish those data immediately. But it will be interesting, later, to compare the different patterns and try to understand at least some differences … It would also be interesting to give more time to this small experiment and, for instance, try to capture any impact on mood, productivity, … But this would become a whole different story!

Activity tracker: waist vs. wrist

A few weekends ago, I was challenged by a friend to do more steps than him. Of course, I won 😉 But I noticed he was wearing his activity tracker on his wrist while I was wearing mine on my waist. As I noticed several times before, when I had an activity tracker on my wrist, these devices tend to capture some movements even if you don’t actually walk (while typing energetically on the computer or while driving for instance).

So I took the opportunity of a small trip to wear 2 activity trackers, one Fitbit One on my waist and one Fitbit Charge HR on my wrist. Continue reading “Activity tracker: waist vs. wrist” →

Counting steps is the easiest way to reduce cardiovascular risk

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 …

Continue reading “Counting steps is the easiest way to reduce cardiovascular risk” →

Do you gain weight before moving to the USA?

I’ve been using several Fitbit devices since a few years and I decided to stop using them in 2017. My feeling (like many people experienced before) is that wearable devices don’t work. Yes, you’ve read correctly: I was a big supporter of wearables, following the adage “what you can’t measure you can’t manage”, but not anymore.

Why do I write that? What works then? And what does that have to do with the title? Continue reading “Do you gain weight before moving to the USA?” →

Happy New Year 2017!

I wish you all a very happy new year 2017. This time of the year is when you usually do new resolutions. And, among others, I resolved to post more often new ideas and thoughts on at least one broad topics: quantified self.

Continue reading “Happy New Year 2017!” →

2013 in review: how to use your users’ collected data

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).

The second way came from Fitbit, also via e-mail. There was written how many steps I took in total as well as my most and least active periods / days of 2013. At the bottom there was a link to a public page comparing distances traveled in general with what it could mean in the animal kingdom (see below or here). This is not Big Data (although I am sure Fitbit have access to all these data). But at the same time (aggregated) individual data are not shared with the general public (although here again I am sure a similar policy apply to Fitbit users).

Different companies, different ways to handle the data … I hope people will realise the implication of sharing their data in an automated ways in such centralized services.

More sleep with Fitbits

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).

Visually it looks like there is a tendency to sleep more as 2013 passes. But, although the best linear fit shows an angle, the difference between sleep in March and sleep in December is not significant.

R allows to study the data in many different ways (of course!). When plotting the distribution of durations asleep it seems this may be distributed like a normal (Gaussian) distribution (see the graph below). But the Shapiro-wilk normality test shows that the data doesn’t belong to a normal distribution.

As mentioned above, Fitbit devices are tracking other sleep parameters. Among them there is the number of awakenings and the sleep efficiency.

The simple plot of the number of awakenings over time shows the same non-significant trend as the sleep duration (above). The histogram of these awakenings shows a more skewed distribution to the left (to a low number of awakenings) (than the sleep duration). This however shows there is a relation between the two variables: the more I sleep the more the Flex detects awakenings (see second graph below).

Sleep efficiency is the ratio between the total time asleep by the total time in bed from the moment I fell asleep. This is therefore not something related to the different sleep stages. However it may indicate an issue worth investigating with a real doctor. In my case, although I woke up 9 time per night on average in 2013, my sleep efficiency is very high (93.7% on average) …

… or very low. There are indeed some nights where my sleep efficiency is below 10% (see the 4 points at the bottom of the chart). These correspond with nights when I didn’t sleep a lot and also with very little awakenings (since these are related).

There is no mood tracking with Fitbit (except one additional tracker that you can define by yourself and must enter a value manually): everything tracked has to be a numerical value either automatically tracked or manually entered. It would be interesting to couple these tracked variables with the level of fatigue at wake-up time or the mood you feel during the subsequent day. I guess there are apps for that too …

The code is updated on Github (this post is in the sleep.R file).

(*) Note: I just discovered that there is in fact a specific call in the API for time series … This is for a next post!

Getting some sleep out of Fitbits

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.

This graph is what I achieved so far. I didn’t sleep much in March-April 2013: on average 4.9 hours per night. The interesting thing is that I can understand why by going back to my agenda at that time (work, study, family …). As soon as I can get additional data it would be interesting to see if sleep durations will increase later on.

(*) If you know how to get all sleep durations for 2013 in one query, let me know!

2013 with Fitbits

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.

Basically the One is a pedometer (it tracks the number of steps you make per day) but also the number of floors climbed and the time asleep. Note you have to tell your device when you go to sleep and when you wake up ; it will substract automatically the times you were awake. The rest of the data presented are taken from these few observed variables: distance traveled, calories burnt, … The Fitbit website also categorizes your activity from ‘sedentary’ to ‘very active’.

Of course there is an app (for both iOS and Android) where you can also enter what you eat (it automatically calculate the number of calories ingested) and your weight (unless you buy a wifi scale from them). You can set goals on the website and then it tells you how many steps you have to make per day. All this data is stored on a Fitbit server and you can access it via your personal dashboard (yes your data is kept away from you but there are ways to get it …).

I liked the Fitbit One mainly because it is easy to use: you take it and forget it, it works in the pocket. There is a nice, easy to use web interface – great for immediate consumption (not really for long trend analysis). It is quite cheap to acquire the device (well, it is quite small anyway). It works with desktop software as well as mobile app (incl. synchronisation). The One can easily be forgot in a pocket (gives peace of mind) but it doesn’t work when you don’t have pockets (shower, pyjama, changing clothes, … ; I didn’t use the clip/holder at the waist).

That leads me to its disadvantages …

First it’s a proprietary system: you need to pay 50USD in order to get the data you generate, to get your data. Although it makes perfect sense from a business perspective, the device then costs 150USD (and not only 99USD for acquisition alone).
Then it also uses a proprietary interface to charge the device. This is problematic when you move house (the cable is somewhere in a box) or simply when the cable is lost (see messages on Twitter asking for such cable when lost). Most mobile phone manufacturers understood that and provide regular USB interface (for charging and syncing btw). I guess the small form factor has a price to pay.
Tracking of other activities than movement is tedious, especially the need for an internet connection in order to enter food eaten in the app (but otherwise that’s the drawback of logging: auto-vs-manual in general).
Then tracking is sometimes not practical. e.g. between wake up and dressed up or shower. So is there always some under-reporting? Probably there is as I don’t wear it when changing or in pyjama (no pocket). Of course the One comes with an armband-holder but I guess it records data differently.

But the last and main disadvantage that comes to my mind is linked with its advantage: it is so easy to use and to forget (in the washing machine), it can fall and you won’t notice it.

So of course I lost it. It was in a business trip in South-East Asia. I thought I put it in my suitcase when changing pants but I couldn’t find it anymore. So after a few hesitations I chose to get a Fitbit Flex.

The Flex comes in another format: it’s like a small pill that you put in a plastic armband-holder. Therefore it is closer to the body (but not legs, to count steps) and therefore you don’t need pockets. However it doesn’t give time (if you have a watch you’ll have 2 devices at your left wrist? Fitbit now sell an evolution of the Flex – the Force – with LEDs displaying time a.o.). As it is always in its armband I feel it is less likely to be forgotten. And you don’t need pockets, it’s like a bracelet you receive at some concerts. The battery autonomy is approximately the same: around 7 days. You can read here another comparison of the two.

So, what about 2013?

In order to dig the past I could:

use the Fitbit dashboard (see first picture of this post) and visually track what I did, making screenshots as I want to keep some results offline ;
shelve 50USD for the Premium reports that can be downloaded and use whatever software to look at the data – note that you get more than just reports for that ;
use the Fitbit API and figure out how to get my data out it.

Of course I chose the third option. It is a bit more complicated but helped with one of Ben Sidders’post I started coding my “app” in R, the statistical language. As there is a bit more than Ben is explaining I posted all my code on the Github repository of my app, jepsfitbitapp.

The first thing I wanted to see is the most obvious one: my steps. As you can see in the figure below I started to collect data in March 2013 (with the One), I stopped collecting data around October 2013 (when I lost the One) and I re-started later on (with the Flex). I usually walk between 5,000 and 10,000 steps per day, with a maximum on July 1st (the day we moved). 10,000 steps is the daily goal Fitbit gave me. There is a significant difference in the number of steps measured by the One (before October) and the Flex (after October): I cannot really say if it is due to the change in tracking device (and their different location on the body) or if I kind of reduced my physical activity (mainly because of more work, sitting in the office).

As always, I’ll promise to add some physical activity on top of this baseline as a New Year resolution. We’ll see next year how things evolve. In the meantime I’ll explore more what I can extract from my Fitbits in the following posts. Stay tuned!