Abstract
Running is a popular form of physical activity. Personal, social, and environmental determinants influence the engagement of the individual. To get insight in the relation between running behavior and external situations for different types of users, we carried out an extensive data mining study on large-scale datasets. We combined 4 years of historical running data (collected by a mobile exercise application from over 10K participants) with weather, topographical and demographical datasets. We introduce weighted frequent item mining for the analysis of the data. In this way, we capture temporal and environmental situations that frequently associate with different running performances. The results show that specific temporal and environmental situations (hour in a day, day in a week, temperature, distance to residential areas, and population density) influence the running performance of users more than other situational features. Hierarchical agglomerative clustering on the running data is used to split runners in two clusters (with sustained and less sustained running behavior). We compared the two groups of runners and found that runners with less sustained behavior are more sensitive to the environmental situations (especially several weather and location related features, such as temperature, weather type, distance to the nearest park) than regular runners. Further analysis focused on the situational features for the less sustained runners. Results show that specific feature values correspond to a better or worse running distance. Not only the influence of individual features was examined but also the interplay between features. Our findings provide important empirical evidence that the role of external situations in the running behavior of individuals can be derived from analysis of the combined historical datasets. This opens up a large potential to take those situations specifically into consideration when supporting individuals which show less sustained behavior.
Highlights
Physical inactivity has been identified as a leading risk factor for poor health in modern society, as it can lead to serious physical and mental health problems [1, 2]
In contrast to previous data studies in running activity, we considered a variety of features and investigated their association with running performance with respect to different types of users
By combining and analyzing those datasets, we addressed and examined the following three research questions: 1. Which situational features are correlated with the running distance?
Summary
Physical inactivity has been identified as a leading risk factor for poor health in modern society, as it can lead to serious physical and mental health problems [1, 2]. In order to maintain a healthy lifestyle, people are advised to engage in a sufficient amount of physical activity on a regular basis [i.e., at least 150 min moderateintensity activity every week for adults [3]]. A large group of individuals struggle with sustaining this healthy activity level. Searching for ways to promote sustained physical activity for less active individuals is a challenge [5, 6]. Intelligent mobile systems can automatically and accurately track people’s behavior and, based on this tracking, continuously intervene with a user to promote physical activity [7,8,9,10]
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