Playful Data-driven Active Urban Living (PAUL)
In large cities, the health and life expectancy of individuals are worse than in rural environments, partly due to a lower physical activity of citizens. In this proposal we want to understand in more detail how physical activity of city dwellers can be increased by using personalized app technology.
Until now the existing health and exercise apps in app stores lack a scientific base. Different apps use different ways to stimulate people to be physically active (e.g. feedback about physical activity, motivational messages, games). Some researchers argue that what type of app works, is highly individual. This means that the right type of app may depend on e.g. your current physical activity level, health, personality and residential context. However, how to make the optimal match between app and user is still unclear.
This project aims to answer these questions, by developing an exercise app with different forms of feedback. We will also develop a method to find the optimal match between app and user. We will use the app to collect data about the physical activity and location of the users. Since these data is collected every 5 seconds, this will give us very large data sets (big data). We will use ‘data mining’ techniques (automated search programmes on a computer) to find out which app works best for different types of users. We will then give each person his or her optimal app, and measure if the physical activity further increases. Based on the data, we can determine what the effect is of different types of exercise apps, and what app is most suitable for a particular person.