What Are Good Situations for Running? A Machine Learning Study Using Mobile and Geographical Data

Frontiers in Public Health

Marije Deutekom¹  Karlijn Sporrel²  Simon Scheider²  Joris Timmer³  Ben Kröse⁴  Shihan Wang⁵ 

[1] Faculty of Health, Sports and Welfare, Inholland University of Applied Sciences, Amsterdam, Netherlands;Human Geography and Spatial Planning, Utrecht University, Utrecht, Netherlands;Informatics Institute, University of Amsterdam, Amsterdam, Netherlands;Informatics Institute, University of Amsterdam, Amsterdam, Netherlands;Digital Life, Amsterdam University of Applied Sciences, Amsterdam, Netherlands;Informatics Institute, University of Amsterdam, Amsterdam, Netherlands;Information and Computing Sciences, Utrecht University, Utrecht, Netherlands;

关键词: mobile data mining;    physical activity;    running;    machine learning;    big data;    environmental situations;   

DOI  :  10.3389/fpubh.2020.536370

来源: Frontiers

【 摘 要 】

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.

【 授权许可】

CC BY