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Behavioral Periodicity Detection from 24h Wrist Accelerometry and Associations with Cardiometabolic Risk and Health-Related Quality of Life


Matthew Buman, Feiyan Hu, Eamonn Newman, Alan Smeaton, Dana Epstein

Publication Type: 
Refereed Original Article
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.
Digital Object Identifer (DOI): 
Publication Status: 
Date Accepted for Publication: 
Monday, 4 January, 2016
Publication Date: 
BioMed Research International
Research Group: 
Dublin City University (DCU)
Project Acknowledges: 
Open access repository: