Clustering of free-living physical activity patterns and association in mental health studies
Abstract Number:
2883
Submission Type:
Contributed Abstract
Contributed Abstract Type:
Paper
Participants:
YA TING LIANG (1), CHARLOTTE WANG (2), CHUHSING KATE HSIAO (2)
Institutions:
(1) Institute of Epidemiology and Preventive Medicine, National Taiwan University, N/A, (2) Institute of Health Data Analytics and Statistics, National Taiwan University, N/A
Co-Author(s):
CHARLOTTE WANG
Institute of Health Data Analytics and Statistics, National Taiwan University
CHUHSING KATE HSIAO
Institute of Health Data Analytics and Statistics, National Taiwan University
First Author:
YA TING LIANG
Institute of Epidemiology and Preventive Medicine, National Taiwan University
Presenting Author:
Abstract Text:
Monitoring free-living physical activity (PA) can provide valuable insight into daily life activities. However, variations of PA, including the within-subject and between-subject variation, are often large and cause difficulty in analysis. In addition, due to its longitudinal characteristic, it is challenging to summarize and extract interpretable features. In this paper, we propose for such function data an elastic-based clustering algorithm for detecting specific changes in activity patterns. The process of this algorithm includes segmentation, data similarity computation, and pattern clustering. Using this clustering algorithm, we can obtain subject-specific and cluster-specific activity mean functions and perform association analysis to explore the relationship between physical activity and health outcome of interest. This algorithm can detect the phase and amplitude variation, reduce data dimension and offer interpretable findings. The proposed method is demonstrated on mental health studies. The results provide cluster-specific patterns for physical activities and can describe the patterns that are associated with the phy
Keywords:
wearable device|functional data analysis|clustering|free-living physical activity| |
Sponsors:
Biometrics Section
Tracks:
Personalized/Precision Medicine
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