Periodicities (repeating patterns) are observed in many human behaviors. Their strength may capture untapped patterns that incorporate sleep, sedentary, and active behaviors into a single metric indicative of better health. We present a framework to detect periodicities from longitudinal wrist-worn accelerometry data. GENEActiv accelerometer data were collected from 20 participants (17 men, 3 women, aged 35–65) continuously for 64.4±26.2 (range: 13.9 to 102.0) consecutive days. Cardiometabolic risk biomarkers and health-related quality of life metrics were assessed at baseline.
Download count: 0
Details
- Buman, Matthew (Contributor)
- Hu, Feiyan (Author)
- Newman, Eamonn (Author)
- Smeaton, Alan F. (Author)
- Epstein, Dana R. (Author)
- College of Health Solutions (Contributor)
- 2016-01-04
- Digital object identifier: 10.1155/2016/4856506
- Identifier TypeInternational standard serial numberIdentifier Value2314-6133
- Identifier TypeInternational standard serial numberIdentifier Value2314-6141
- The article is published at https://www.hindawi.com/journals/bmri/2016/4856506/, opens in a new window
Citation and reuse
Cite this item
This is a suggested citation. Consult the appropriate style guide for specific citation guidelines.
Buman, M. P., Hu, F., Newman, E., Smeaton, A. F., & Epstein, D. R. (2016). Behavioral Periodicity Detection from 24 h Wrist Accelerometry and Associations with Cardiometabolic Risk and Health-Related Quality of Life. BioMed Research International, 2016, 1-9. doi:10.1155/2016/4856506