This is our story about Coach.

“How could environmental data start to influence when and where people go for their outdoor activities?”

During the minor User Interface and User Experience Design we have worked on a project for the Dutch agency CLEVER°FRANKE. Against the backdrop of globalization, cities are increasingly challenged to maintain sustainable economic development and a livable urban environment that ensures quality of life for its inhabitants. Whether big or small, it will be ‘smart cities’ making the difference in the future. Part of these smart cities will be sensor networks that continuously monitor the quality of the environment. The goal of our assignment was to design how environmental data could influence when and where people go for their outdoor activities.

In the months followed we researched how to communicate the various available measurements and how to allow people to choose what variables to take into account. A big challenge was how autonomous the product was going to be. 

During this phase we spoke to our target group and a number of professionals in different relations to our target group. Through them we gained valuable insights and feedback.

Based on the user needs we came up with three different concepts. Together with CLEVER°FRANKE we decided to develop a concept focussing on when to go for a run. In this case study you will find more about our process and how we came up with Coach.

Omlijning wit onderkantOmlijning wit onderkant

Marian Vijverberg
visual design

Mike den Hertog

Jan Teunissen
interaction design

We are team Hopscotch: four Communication and Multimedia Design students at the Rotterdam University of Applied Sciences, all at the beginning of our careers. In 2012 we started our Bachelor study and during the years we learned to build digital products for all kinds of audiences. This is our second big project working together as a team. Still friends.

For our research we spoke to experts from different kinds of organizations, interviewed multiple sport app users and conducted a broad range of desk research. All of this resulted in statement cards about users, data visualisation and enviromental data.

After the discovery phase we came up with three different concepts, all focusing on different user needs. To make a decision which concepts suits the user the best we interviewed several users and discussed our insights with the client.

Where to go
This concept is based on the need of users to start an activity from home. The function will be visible when the user has already decided to go for, for example a run. Beforehand the app gives you a direction to go to based on environmental data.

When to go
Based on Runkeeper’s intake, this concept  will help users plan their outdoor activities according to their individual schedule. For example, if you have a pollen allergy, this will be kept in mind while planning your outdoor activity.

How to go
This concept allows users to have a laid-back attitude towards environmental factors influencing their health and activity. You will receive a route based on your preferences and past runs from other users. 

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We decided, together with CLEVER°FRANKE, that we should focus on when to run. This concept called Coach was simply our strongest concept, and seemed to have the most interesting challenges in datavisualization. We all concluded our concept needed an iteration. We wanted to make a big difference for our user, and since our target group was previously very big, we decided to change our focus to a specific target group: asthmatici. This is why:

Estimated amount of asthmatics
over the world

Asthma patients
in the Netherlands

€ 286.914.000

Total healthcare costs of asthma
in 2012 (The Netherlands)

Of all are childeren


The total amount of hospitalizations
caused by asthma in 2012


Expected growth of costs
in the upcoming 20 years

€ 0,7+ bilion

Expected total costs
in 20 years from now

This choice and change of focus had a lot of impact on our concept, as well as our process. We needed to go back in to our research and replete this with more knowlegde about our target group and their disease. Because asthma is a disease with a lot of varieties, a different approach of environmental data was needed. We want each individual user to get the most out of our app, so we need to get to know each individual kind of asthma. 

So, to compose an accurate and personal index based on the individual user, we defined a formula in which we consider the completed intake and the current data. Besides the general limits, which apply to everyone, the formula formulates personal limits for each aspect. There is for example an recommended limit of PM10 exposure for everyone, but when the user is more sensitive the limit will be adjusted.

* Data from World Health Organisation

Let us introduce you to...

Designing conversations

Not just a form


To give Coach more meaning we chose to approach the user onboarding from a different perspective. With a chat we encourage the user to get introduced with Coach in a personal way. Also it feels more positive when talking about the aspects Coach should consider while finding the best running moments.

Finding triggers

Multivariate testing


To find out the best way to ask asthma triggers, we did multivariate testing. We tested three ways of approaching the trigger question. The first one was selecting the most relevant triggers. An other option was to swipe trigger statements on relevance. The last option was an rating system for each seperate variable. We made an link which sends the participant to a random option. The last question was to relate to one of the three types of asthma. In this way we could conclude which approach is most suitable.


We decided to go for the radar chart since the responses of this type of question matched the best with the person’s type of asthma. The user will start with a brief overview of what is expected. After filling out their triggers, Coach will show the user what has been answered and asks for a confirmation. By filling out this chart the user enables us to make an accurate and personal advice.

Finding triggers

Communicating timeslots


After a notification has been sent to the user, personal data will be shown in this first level of information. This index combines real time data with the trigger values received during the intake. The best running timeslots will be highlighted. On the other hand Coach indicates the moments that are not advisable.

Consult underlying raw data


To show our user how their index is structured, we created a second data layer. This layer could be entered by rotating the device. In this state our user can easily see an overview of the different factors their index is built upon. To get a closer view of the data, each trigger could be highlighted one at a time, to see the scale and the recommended limit. This given limit is personal because it depends on the completed intake and previous running experiences under certain circumstances.

Believe in improvement

Learning from experiences


After the run the app will ask a reflective question considering the completed run. This question depends on the circumstances in which the activity is done. In this way the app specify what influence different triggers have on the users and therefore give better suggestions. We chose a neutral tone of voice and we tried not to be pedantic about the chosen running moment.

Leave your findings and get tips


The given answer on the reflective question will appear in the notes field. Here the user gets the chance to amplify there experience. The progress of your own performance and experience will be easily documentated. In this way the user will be more aware of the impact of environmental data on sport activities.
Also the app gives simple, yet usefull, tips for an healthier run, considering your completed activities. 

During our design process we made multiple prototypes. While doing this, we explored our options by making them in different prototyping tools, such as Axure, Pixate and InVision. We came to the conclusion that all tools were coming short in terms of our requirements. That’s why we decided to start building our final prototype in Xcode.


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Special thanks to

Hans Berkhout - RIVM ・ Hans Coli ・ Wouter van Dijk - CLEVERºFRANKE ・ Gert Franke - CLEVERºFRANKE ・ Jan Hoogeveen - CLEVERºFRANKE ・ Sander van Houdt - Blendle ・ Jonatan Königs ・ Marcel van der Kuil - Life Long Running ・ Peter Kun - Rotterdam University of Applied Science ・ Koen van Niekerk - VanBerlo ・ Anne Nigten - Creating010 ・ Andrew Smit ・ Joost Plattel - Data strategistStadslab luchtkwaliteit ・ Christine Strous - Dutch Longfonds ・ Peter van Veldhoven - Sports Doctor MCH ・ Amarens Yntema - CLEVERºFRANKE ・ Mom & dad

hopscotch - 2016