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Clustering physical activity phenotypes using the ATLAS index on accelerometric data from an epidemiologic cohort
1Peter L. Reichertz Institute for Medical Informatics, University of Braunschweig - Institute of Technology and Hannover Medical School, Hannover, Germany.
Studies in Health Technology and Informatics
|August 28, 2014
Summary
This study successfully identified distinct physical activity phenotypes, including
Area of Science:
- Epidemiology
- Physical Activity Research
- Biostatistics
Background:
- Numerous studies link physical activity to mortality and morbidity.
- However, the impact of varying physical activity dose-patterns remains poorly understood.
Purpose of the Study:
- To determine if physical activity phenotypes can be extracted from large-scale epidemiological accelerometer data.
- To validate these phenotypes against proposed classifications using the ATLAS index.
Main Methods:
- Utilized the ATLAS index to compute parameters from 6386 NHANES 2005-2006 cohort datasets.
- Applied x-Means clustering to the computed ATLAS parameters to identify distinct groups.
Main Results:
- Identified four distinct physical activity phenotypes: 'insufficiently active', 'irregularly active', 'busy bee', and 'physical worker/weekend warrior'.
- Demonstrated the feasibility of extracting activity phenotypes using the ATLAS index.
Conclusions:
- The ATLAS index enables the identification of diverse physical activity phenotypes.
- Further research is needed to explore the regularity component and the association between activity patterns and health outcomes in identified groups.
