Login (DCU Staff Only)
Login (DCU Staff Only)

DORAS | DCU Research Repository

Explore open access research and scholarly works from DCU

Advanced Search

Behavioral periodicity detection from 24h wrist accelerometry and associations with cardiometabolic risk and health-related quality of life

Buman, Matthew P., Hu, Feiyan orcid logoORCID: 0000-0001-7451-6438, Newman, Eamonn orcid logoORCID: 0000-0002-0310-0539, Smeaton, Alan F. orcid logoORCID: 0000-0003-1028-8389 and Epstein, Dana R. (2016) Behavioral periodicity detection from 24h wrist accelerometry and associations with cardiometabolic risk and health-related quality of life. BioMed Research International, 2016 . p. 4856506. ISSN 2314-6141

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 (range: 13.9 to 102.0) consecutive days. Cardiometabolic risk biomarkers and health-related quality of life metrics were assessed at baseline. Periodograms were constructed to determine patterns emergent from the accelerometer data. Periodicity strength was calculated using circular autocorrelations for time-lagged windows. The most notable periodicity was at 24 h, indicating a circadian rest-activity cycle; however, its strength varied significantly across participants. Periodicity strength was most consistently associated with LDL-cholesterol (’s = 0.40–0.79, ’s < 0.05) and triglycerides (’s = 0.68–0.86, ’s < 0.05) but also associated with hs-CRP and health-related quality of life, even after adjusting for demographics and self-rated physical activity and insomnia symptoms. Our framework demonstrates a new method for characterizing behavior patterns longitudinally which captures relationships between 24 h accelerometry data and health outcomes.
Item Type:Article (Published)
Subjects:Computer Science > Lifelog
Medical Sciences > Biomechanics
Medical Sciences > Health
DCU Faculties and Centres:Research Institutes and Centres > INSIGHT Centre for Data Analytics
DCU Faculties and Schools > Faculty of Engineering and Computing > School of Computing
Official URL:http://dx.doi.org/10.1155/2016/4856506
Copyright Information:© 2016 The Authors
Use License:This item is licensed under a Creative Commons Attribution-NonCommercial-Share Alike 3.0 License. View License
Funders:Science Foundation Ireland, EU FP7, Virginia G. Piper Charitable Trust, Arizona State University/Dublin City University Catalyst Fund
ID Code:21076
Deposited On:05 Feb 2016 11:27 by Alan Smeaton . Last Modified 11 Oct 2018 12:50

Full text available as:

[thumbnail of 4856506.pdf]
PDF - Requires a PDF viewer such as GSview, Xpdf or Adobe Acrobat Reader


Downloads per month over past year

Archive Staff Only: edit this record