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Becoming big brother: an introduction to the Quantified Self

  • Workplace Data & Insights

Justin Timmer is our In-House Researcher. Contact him via

About five years ago, I first became a real “Measure Man”, without being aware of the existence of my future employer. At the time, I studied Sports and Health and via the University’s network, I learned about the Quantified Self Institute, which immediately grabbed my attention. Quantified Self is about measuring your life and experimenting to improve and understand your own life.

The first step was to get started with acquiring data: I used wearable technology, apps, and a simple pen and paper to measure my happiness, work, heart rate, sleep, and many other things. Soon after, I became a researcher for the Quantified Self Institute and got involved with the Healthy Workplace project. This project was initiated by Measuremen and 5 other partners to research and support employees with healthy workplace behaviour. In this project, we use workplace sensors, wearables, and other techniques to increase awareness and stimulate behaviour when it comes to health, and health behaviour. More information about this initiative can be found in our Innovation Lab. In this article, I will discuss the basic concepts of the Quantified Self and share some unique insights that we’ve gathered with our Healthy Workplace initiative.


Usually, we are not that busy with our past and the things that we’ve learned. We tend to look forward: what deadlines need to be met, what our schedule will be next week or the party we will attend during the weekend. Like many of the people I spoke to (in particular building managers), I wasn’t really aware of how I was doing. I would only go to the doctor when I was ill, but by then it was obviously already too late. Furthermore, quite some research showed that diary writing is very healthy, so I decided to start a quantitative diary.

Every day I have a sleeping diary, a working dairy, and an evening diary where I track many things, from my personal feelings throughout the day to the professional success of the day. I see it as a daily check-up and reflection, that helps me to spot trends and make predictions. For example, I measure my body weight every day, not that I’m afraid of being fat, but to be sure that I could adapt my behaviour before it’s too late. My data shows me how I’m feeling in all the aspects of my life, making me more aware of patterns and the relations between them. When I spot downward trends, I can prioritise these and take action.

Visualizing the data to get the right insights

Measuring is very nice, but measuring by itself does nothing much to behaviour (although some Quantum Physicists might disagree). It is hard to learn something from huge sheets of numbers. You need to visualise your data to get key insights into what is going on with you or your organisation. Only if you do this properly, you can change your behaviour. This is the key to my hobby and the work I’m doing at Measuremen. If you make the wrong graphs, you’ll draw the wrong conclusions.

Surprising graph

It depends on your initial awareness whether your graphs surprise you after measuring yourself for the first time. Some graphs will feel like a “Duh!”, while others make you gasp for a bit. Although I thought I was quite aware of my own life, my data has often surprised me. I was extremely surprised when I learned that stress was good for my productivity, I owned 273 pieces of clothing, spend 75 minutes on my phone each day, and only eat for 55% of the time when I’m hungry. Often, we believe that scientific articles tell the truth about all of us, but this is hardly the case, science doesn’t tell anything about our personal life. We all individually differ from the average. Our behaviour needs personalized feedback based on personal statistics to support our behaviour appropriately.

Deep insights for targeted behaviour change

But how does learning about yourself supports you to change? To get a feel for that, I’ll take you along with my personal quantified self data. In the next graph, I plotted the past three years of my steps data. This almost feels like a data overload, but you’ll see that I hit the 10.000 steps goal quite frequently. But, as you can also see, my score is far from perfect. My first reaction to this graph would be; “Justin move just a little more to hit that goal!”. But this is still a general graph. If you dive deeper and combine data, you can get a more detailed analysis of what could give me tailored feedback.

When I select my workdays and split the data on specific workplaces you’ll get the bar graph below. The data shows that the workplace heavily influences the amount of movement during the day. In most workplaces, I reach about 10.000 steps each day. However, when I am at home I walk only about 6000 steps, which is a shocking 40% fewer steps compared to the other locations. In short, there is a lot to gain when I’m working at home!

Going even further, I decided to try and quantify my happiness. I started by outlining the factors that make me happy, by doing regression analyses from my diaries. From these analyses, I learned that my loneliness is a larger predictor than my health. By looking at the change of my data over time, I can see the relationships between variables. Like between my perceived stress and productivity, or how all the aspects of my life are interconnected. For example, I learned that there are quite strong relationships with the number of meetings I have on a day and the stress I experience that day. This makes me wonder about rescheduling my daily meeting behaviour.


Having data about yourself gives you many possibilities to learn about yourself and change your behaviour for the better. I can spend hours diving into my data and understanding my own life and all its intricacies. People often ask me: how does it help you in your day-to-day life? I like to show them the graph below, which outlines my reported happiness. And over the past years, it shows a steady increase. Secondly, my explorations got me to understand life in general on much deeper and broader levels, which gave me a sense of meaning.

At Measuremen, I work as an in-house researcher, and do basically the same thing but then at an organisation level. I analyse the data of the organisations we measure and help them to understand themselves. Furthermore, I work at Measuremen on the development of Habital, the app that uses similar data to get insights into the behaviour of employees and provides them insights into their own behaviour. I honestly believe that data can give meaningful insights that support you in making good evidence-based decisions. I’m happy to work on that, for myself, and for all our clients.

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