Characterisation of Temporal Patterns in Step Count Behaviour from Smartphone App Data: An Unsupervised Machine Learning Approach

Francesca Pontin, Nik Lomax, Graham Clarke and Michelle A. Morris
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Francesca Pontin: Leeds Institute for Data Analytics, University of Leeds, Leeds LS2 9ET, UK
Nik Lomax: Leeds Institute for Data Analytics, University of Leeds, Leeds LS2 9ET, UK
Graham Clarke: School of Geography, University of Leeds, Leeds LS2 9ET, UK
Michelle A. Morris: Leeds Institute for Data Analytics, University of Leeds, Leeds LS2 9ET, UK

IJERPH, 2021, vol. 18, issue 21, 1-27

Abstract: The increasing ubiquity of smartphone data, with greater spatial and temporal coverage than achieved by traditional study designs, have the potential to provide insight into habitual physical activity patterns. This study implements and evaluates the utility of both K-means clustering and agglomerative hierarchical clustering methods in identifying weekly and yearlong physical activity behaviour trends. Characterising the demographics and choice of activity type within the identified clusters of behaviour. Across all seven clusters of seasonal activity behaviour identified, daylight saving was shown to play a key role in influencing behaviour, with increased activity in summer months. Investigation into weekly behaviours identified six clusters with varied roles, of weekday versus weekend, on the likelihood of meeting physical activity guidelines. Preferred type of physical activity likewise varied between clusters, with gender and age strongly associated with cluster membership. Key relationships are identified between weekly clusters and seasonal activity behaviour clusters, demonstrating how short-term behaviours contribute to longer-term activity patterns. Utilising unsupervised machine learning, this study demonstrates how the volume and richness of secondary app data can allow us to move away from aggregate measures of physical activity to better understand temporal variations in habitual physical activity behaviour.

Keywords: physical activity; unsupervised machine learning; smartphone; secondary data; cluster analysis; data science; big data; self-recorded health data (search for similar items in EconPapers)
JEL-codes: I I1 I3 Q Q5 (search for similar items in EconPapers)
Date: 2021
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (2)

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