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Physical Activity descriptors for Cardiometabolic Health

Friday,  June 12, 2020

Earlier this year I started a new project with Dr. Séverine Sabia at the French National Institute for Health Research (INSERM). I have successfully collaborated with Dr. Sabia in the past. Our new project explores how novel descriptors of accelerometer data relate to cardio metabolic health.

Time series extraction

My involvement is to help implement these novel descriptors in R package GGIR. A first step was to revise the existing software code to extract cleaned time series data. For example, we did not want to rely on sleep detection for the first recording night, but we considered it valuable to have some estimate of waking-up time on the second recording day. So, we had to decide what information about the first night to trust and use for that. Further, we want to exclude nights for sleep analysis when an accelerometer is not worn. However, we want to include those nights for 24 hour time-use analysis if sleep diary indicates that the accelerometer was only not worn during sleep.

Next steps

The next step in the project will be to use these time series as input for various behavioural descriptors. The technique I will mainly focus on is the behavioral fragmentation analysis as most recently implemented in R package ActFrag by Junrui Di and colleagues (https://doi.org/10.1101/182337). The plan is to implement these metrics in GGIR and explore opportunities for improvement.

 

Physical Activity descriptors for Cardiometabolic Health
Photo: by Paul Rysz on unsplash
Physical Activity descriptors for Cardiometabolic Health
Photo: by Hush Naidoo on Unsplash