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Lookup NU author(s): Dr Vincent van Hees,
Dr Kirstie Anderson,
Professor Mike TrenellORCiD
This work is licensed under a Creative Commons Attribution 4.0 International License (CC BY 4.0).
© 2018, The Author(s). Wrist worn raw-data accelerometers are used increasingly in large-scale population research. We examined whether sleep parameters can be estimated from these data in the absence of sleep diaries. Our heuristic algorithm uses the variance in estimated z-axis angle and makes basic assumptions about sleep interruptions. Detected sleep period time window (SPT-window) was compared against sleep diary in 3752 participants (range = 60–82 years) and polysomnography in sleep clinic patients (N = 28) and in healthy good sleepers (N = 22). The SPT-window derived from the algorithm was 10.9 and 2.9 minutes longer compared with sleep diary in men and women, respectively. Mean C-statistic to detect the SPT-window compared to polysomnography was 0.86 and 0.83 in clinic-based and healthy sleepers, respectively. We demonstrated the accuracy of our algorithm to detect the SPT-window. The value of this algorithm lies in studies such as UK Biobank where a sleep diary was not used.
Author(s): van Hees VT, Sabia S, Jones SE, Wood AR, Anderson KN, Kivimaki M, Frayling TM, Pack AI, Bucan M, Trenell MI, Mazzotti DR, Gehrman PR, Singh-Manoux BA, Weedon MN
Publication type: Article
Publication status: Published
Journal: Scientific Reports
Online publication date: 28/08/2018
Acceptance date: 08/08/2018
Date deposited: 01/10/2018
ISSN (electronic): 2045-2322
Publisher: Nature Publishing Group
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