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

Shihan Wang1,2, Simon Scheider3, Karlijn Sporrel3

  • 1Informatics Institute, University of Amsterdam, Amsterdam, Netherlands.

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Summary

Environmental factors significantly impact running behavior, especially for less consistent runners. Understanding these external situations can help support individuals to maintain their running routines.

Keywords:
big dataenvironmental situationsmachine learningmobile data miningphysical activityrunning

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Area of Science:

  • Exercise Science
  • Data Mining
  • Environmental Psychology

Background:

  • Running is a prevalent physical activity influenced by personal, social, and environmental factors.
  • Understanding how external situations affect running engagement is crucial for user support.

Purpose of the Study:

  • To investigate the relationship between running behavior and external situational factors using large-scale datasets.
  • To identify temporal and environmental determinants of running performance across different user types.

Main Methods:

  • Data mining techniques, including weighted frequent item mining, were applied to four years of running data from over 10,000 participants.
  • Running data was combined with weather, topographical, and demographic datasets.
  • Hierarchical agglomerative clustering was used to categorize runners based on behavior sustainability.

Main Results:

  • Specific temporal (time of day, day of week) and environmental (temperature, proximity to residential areas, population density) factors significantly influence running performance.
  • Runners with less sustained behavior demonstrated higher sensitivity to environmental conditions like weather type and park proximity.
  • Analysis revealed specific feature values correlating with better or worse running distances for less sustained runners, highlighting feature interplay.

Conclusions:

  • External situations play a significant role in individual running behavior, derivable from combined historical datasets.
  • Identifying situational influences offers potential for targeted interventions to support individuals with less sustained running habits.
  • Personalized support strategies can be developed by considering the interplay of environmental and temporal factors on running performance.