Leeds Institute for Data Analytics, University of Leeds, Leeds LS2 9ET, UK.
School of Geography, University of Leeds, Leeds LS2 9ET, UK.
Int J Environ Res Public Health. 2021 Oct 31;18(21):11476. doi: 10.3390/ijerph182111476.
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.
智能手机数据的普及程度不断提高，其空间和时间覆盖范围比传统研究设计更广，这有可能深入了解习惯性体育活动模式。本研究实施并评估了 K 均值聚类和凝聚层次聚类方法在识别每周和全年体育活动行为趋势方面的效用。描述行为聚类中人口统计学特征和活动类型的选择。在确定的所有七个季节性活动行为聚类中，夏令时被证明在影响行为方面起着关键作用，夏季活动增加。对每周行为的调查确定了六个具有不同作用的聚类，工作日与周末相比，对符合体育活动指南的可能性有影响。同样，不同聚类之间的体育活动类型偏好也有所不同，性别和年龄与聚类成员身份密切相关。研究还发现了每周聚类和季节性活动行为聚类之间的关键关系，展示了短期行为如何影响长期活动模式。本研究利用无监督机器学习，展示了二级应用程序数据的数量和丰富度如何使我们能够摆脱对体育活动的总体衡量，以更好地了解习惯性体育活动行为的时间变化。
