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Author Hu, Feiyan ♦ Newman, Eamonn ♦ Buman, Matthew P. ♦ Smeaton, Alan F. ♦ Epstein, Dana R.
Source Directory of Open Access Journals (DOAJ)
Content type Text
Publisher Hindawi Limited
File Format HTM / HTML
Date Created 2016-02-17
Copyright Year ©2016
Language English
Subject Domain (in LCC) R
Subject Keyword Medicine
Abstract 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. 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 (r’s = 0.40–0.79, P’s < 0.05) and triglycerides (r’s = 0.68–0.86, P’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.
ISSN 23146133
Age Range 18 to 22 years ♦ above 22 year
Educational Use Research
Education Level UG and PG ♦ Career/Technical Study
Learning Resource Type Article
Publisher Date 2016-01-01
e-ISSN 23146133
Journal BioMed Research International
Volume Number 2016


Source: Directory of Open Access Journals (DOAJ)